Earth Observation for Sustainable Development
Urban Development Project
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 685761.
ESA Ref: AO/1-8346/15/I-NB
Doc. No.: City Operations Report
Issue/Rev.: 1.1
Date: 19.11.2019
EO4SD-Urban Project: Dakar City Report
Partners: Financed by:
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Consortium Partners
No. Name Short Name Country
1 GAF AG GAF Germany
2 Système d'Information à Référence Spatiale SAS SIRS France
3 GISAT S.R.O. GISAT Czech Republic
4 Egis SA EGIS France
5 Deutsche Luft- und Raumfahrt e. V DLR Germany
6 Netherlands Geomatics & Earth Observation B.V. NEO The Netherlands
7 JOANNEUM Research Forschungsgesellschaft mbH JR Austria
8 GISBOX SRL GISBOX Romania
Disclaimer:
The contents of this document are the copyright of GAF AG and Partners. It is released by GAF AG
on the condition that it will not be copied in whole, in section or otherwise reproduced (whether by
photographic, reprographic or any other method) and that the contents thereof shall not be divulged to
any other person other than of the addressed (save to the other authorised officers of their organisation
having a need to know such contents, for the purpose of which disclosure is made by GAF AG)
without prior consent of GAF AG.
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Summary
This document contains information related to the provision of geo-spatial products from the European
Space Agency (ESA) supported project “Earth Observation for Sustainable Development” Urban
Applications (EO4SD-Urban) to the benefit of Global Platform for Sustainable Cities (GPSC)
programme implemented for the City of Dakar and Senegalese authorities.
Affiliation/Function Name Date
Prepared SIRS
NEO
JR
S. Delbour, D. Fretin,
V. Gastal
F. Fang
M. Hirschmugl, H.
Proske
26/09/2019
Reviewed SIRS C. Sannier 27/09/2019
Approved GAF AG, Project Coordinator T. Haeusler 02/10/2019
The document is accepted under the assumption that all verification activities were carried out
correctly and any discrepancies are documented properly.
Distribution
Affiliation Name Copies
ESA Z. Bartalis electronic copy
Government agencies M. Ndaw
F. Cheikh
M. Diara
electronic copy
UNIDO M. Draeck
K. Barunica
electronic copy
Document Status Sheet
Issue Date Details
1.0 02/10/2019 First Document Issue
1.1 19/11/2019 Addition of section 4.4 Sustainable Development Goal 11 Indicators
Document Change Record
# Date Request Location Details
1 02/10/2019 Initial version
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Executive Summary
The European Space Agency (ESA) has been working closely together with the International Finance
Institutes (IFIs) and their client countries to demonstrate the benefits of Earth Observation (EO) in the
IFI development programmes. Earth Observation for Sustainable Development (EO4SD) is a new
ESA initiative, which aims to achieve an increase in the uptake of satellite-based information in the
regional and global IFI programmes. The overall aim of the EO4SD Urban project is to integrate the
application of satellite data for urban development programmes being implemented by the IFIs or
Multi-Lateral Development Banks (MDBs) with the developing countries. The overall goal will be
achieved via implementation of the following main objectives:
• To provide a service portfolio of Baseline and Derived urban-related geo-spatial products
• To provide the geo-spatial products and services on a geographical regional basis
• To ensure that the products and services are user-driven
This Report describes the generation and the provision of EO-based information products to the GEF
supported programme “Global Platform for Sustainable Cities” for Senegal and the counterpart City
authorities in Dakar. The Report provides a Service Description by referring to the user-driven service
requirements and the associated product list with the detailed product specifications. The following
products were requested:
• Urban and Peri-Urban Land Use/ Land Cover and Changes
• Settlement Extent and Imperviousness and Changes
• Urban Green Areas and Changes
• Flood Hazard and Risk Assessment
The current Version of this Report contains the description of the generation and delivery of each
requested product, especially the Land Use/Land Cover (LU/LC) and the LU/LC Changes between
2006 and 2018. The Urban Green Areas and Flood Hazard and Risk Assessment study conducted, and
the resulting products are also described in detail in this Report.
This City Operations Report for Dakar systematically reviews the main production steps involved and
importantly highlights the Quality Control (QC) mechanisms involved; the steps of QC and the
assessment of quality is provided in related QC forms in the Annexe of this Report. Standard
analytical work undertaken with the products can be further included as inputs into further urban
development assessments, modelling and reports.
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Table of Contents
1 GENERAL BACKGROUND OF EO4SD-URBAN ................................................................... 1 2 SERVICE DESCRIPTION........................................................................................................... 1
2.1 STAKEHOLDERS AND REQUIREMENTS ..................................................................................... 1
2.2 SERVICE AREA SPECIFICATION ............................................................................................... 2
2.3 PRODUCT LIST AND PRODUCT SPECIFICATIONS ...................................................................... 5
2.4 LAND USE/LAND COVER NOMENCLATURE ............................................................................. 5
2.5 WORLD SETTLEMENT EXTENT ................................................................................................ 9
2.6 PERCENTAGE IMPERVIOUS SURFACE .................................................................................... 10
2.7 URBAN GREEN AREAS NOMENCLATURE .............................................................................. 10
2.8 TERMS OF ACCESS ................................................................................................................. 10
3 SERVICE OPERATIONS .......................................................................................................... 11
3.1 SOURCE DATA ....................................................................................................................... 11
3.2 PROCESSING METHODS ......................................................................................................... 12
3.3 ACCURACY ASSESSMENT OF MAP PRODUCTS ...................................................................... 12
3.3.1 Accuracy Assessment of the LU/LC Products ................................................................................ 12
3.3.2 Accuracy Assessment of the World Settlement Extent Product ...................................................... 18
3.3.3 Accuracy Assessment of the Percentage Impervious Surface Product ............................................ 20
3.3.4 Accuracy Assessment of Urban Green Areas Product .................................................................... 22
3.3.5 Accuracy Assessment of Flood Extent Product .............................................................................. 23
3.4 QUALITY CONTROL/ASSURANCE .......................................................................................... 25
3.5 METADATA ............................................................................................................................ 26
4 ANALYSIS OF MAPPING RESULTS ..................................................................................... 27
4.1 SETTLEMENT EXTENT – DEVELOPMENTS 2000, 2005, 2010 AND 2015 ................................ 27
4.2 LAND USE / LAND COVER 2003/2006 AND 2018 ................................................................... 29
4.2.1 LU/LC Mapping for Core City Area ............................................................................................... 29
4.2.2 Spatial Distribution of Main LU/LC Change Categories for Core City Area ................................. 33
4.2.3 LU/LC Mapping for Larger Urban Area ......................................................................................... 35
4.2.4 Spatial Distribution of Main LU/LC Change Categories for Larger Urban Area ........................... 37
4.3 URBAN GREEN AREAS ........................................................................................................... 40
4.4 SUSTAINABLE DEVELOPMENT GOAL 11 INDICATORS ........................................................... 42
4.4.1 SDG 11 Indicator 11.2.1 ................................................................................................................. 43
4.4.2 SDG 11 Indicator 11.3.1 ................................................................................................................. 44
4.4.3 SDG 11 Indicator 11.7.1 ................................................................................................................. 46
4.5 CONCLUDING POINTS ............................................................................................................ 47
5 FLOOD HAZARD AND RISK ASSESSMENT ....................................................................... 48
5.1 GENERAL CHARACTERISTICS OF THE STUDY AREA .............................................................. 49
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5.2 FLOOD HISTORY .................................................................................................................... 58
5.3 EO DATA USED ..................................................................................................................... 62
5.4 SHORT DESCRIPTION OF METHODOLOGICAL APPROACH ....................................................... 63
5.5 PRODUCT DESCRIPTION AND ACCURACY ASSESSMENT ....................................................... 67
5.6 RESULTS ................................................................................................................................ 69
6 REFERENCES ............................................................................................................................ 75
Annexes
Annex 1: AOI calculation based on the DG Regio approach
Annex 2: Processing methods for EO4SD-Urban products
Annex 3: Filled Quality Control Sheets
List of Figures
Figure 1: Illustration of Core City and Larger Urban Areas of Dakar. ................................................... 4 Figure 2: Mapping result of the Core City Area of Dakar of the year 2018 overlaid with randomly
distributed sample points used for accuracy assessment. ...................................................................... 15 Figure 3: Mapping result of the Larger Urban Area of Dakar of the year 2018 overlaid with randomly
distributed sample points used for accuracy assessment. ...................................................................... 16 Figure 4: Result of the Urban Green Area mapping in Dakar (change product) with sampling points
used for product validation. ................................................................................................................... 22 Figure 5: Result of the Flood extent mapping in Dakar with sampling points used for product
validation. .............................................................................................................................................. 24 Figure 6: Quality Control process for EO4SD-Urban product generation. At each intermediate
processing step output properties are compared against pre-defined requirements. ............................. 25 Figure 7: Settlement Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015
in Dakar and surrounding region. .......................................................................................................... 27 Figure 8: Settlement Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015
in Dakar within the High Density Area. ................................................................................................ 28 Figure 9: Core City Area - Detailed LU/LC 2018 in Dakar .................................................................. 29 Figure 10: Core City Area - Insight on the detailed Land Use Land Cover 2018 inside the city.......... 30 Figure 11: Core City Area - Detailed LU/LC 2006 structure, in % (left) and km2 (right). ................... 31 Figure 12: Core City Area - Detailed LU/LC 2018 structure, in % (left) and km2 (right). ................... 31 Figure 13: Core City Area – LU/LC change types and spatial distribution .......................................... 33 Figure 14: Core City Area – LU/LC Change types between 2006 and 2018 presented in % (left) and
sqkm (right). .......................................................................................................................................... 34 Figure 15: Larger Urban area – LU/LC 2018 in Dakar. ........................................................................ 35 Figure 16: Larger Urban Area - Insight on the Land Use Land Cover 2018 on the urban fringe. ........ 35 Figure 17: Larger Urban Area - Detailed LU/LC 2006 structure presented in % (left) and km2 (right).
............................................................................................................................................................... 36 Figure 18: Larger Urban Area - Detailed LU/LC 2018 structure presented in % (left) and km2 (right).
............................................................................................................................................................... 36 Figure 19: Larger Urban Area – LU/LC Change types and spatial distribution. .................................. 38 Figure 20: Larger Urban Area – LU/LC Change types 2006 -2018 area in % (left) and sqkm (right) . 38 Figure 21: Urban Green Areas changes and spatial distribution. .......................................................... 40 Figure 22: Status and change of urban green areas in-between 2004/2006 and 2018 expressed in %. . 41 Figure 23: Status and change of urban green areas in-between 2004/2006 and 2018 expressed in area.
............................................................................................................................................................... 41
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Figure 24: Proportion of population with convenient access to public transport. ................................. 43 Figure 25: Ratio of land consumption rate to population growth rate between 2005 and 2015. ........... 44 Figure 26: Percentage change of population and land consumption between 2005 and 2015. ............. 45 Figure 27: Average share of the built-up area that is open space for public use. .................................. 47 Figure 28: Four days after a storm in August 2015, flood waters are still visible in N'Gor Village,
Dakar, Senegal (Photo: Jürgen Fauth, BRACED) ................................................................................. 48 Figure 29: Position of main parts of Dakar (taken from Wikipedia) ..................................................... 49 Figure 30: Dakar, Senegal – Service Area: pink: Core City Area of Interest; green: Larger Urban Area
of Interest (Background Image: Sentinel 2, recorded on 10/10/2016, European Space Agency) ......... 50 Figure 31: Altitudes in Dakar Service Area as derived from available Digital Terrain and Surface
Models (western part: 5m Digital Terrain Model of Dakar (BaseGéo Sénégal,
(http://www.basegeo.gouv.sn/) based on Urban Database (UDB) product; eastern part: ALOS Global
Digital Surface Model "ALOS World 3D - 30m (AW3D30)", version 2.1 (©JAXA): Amthyst areas
indicate most flood prone zones (altitudes below 5 m). ........................................................................ 51 Figure 32: Climate average from 2000 to 2012 in Dakar, Source: World Weather Online .................. 52 Figure 33: Tally Neitty Mbar (main street of Djeddah Thiaroye Kao in the Department of Pikine),
flooded in 2009. Source: Requalification des zones inondés de Djeddah Thiaroye Kao.urbaDTK.org 54 Figure 34: Possible Flooding Area in the New Urban Expansion Area based on Flo2D modelling
results (taken from JICA 2016) ............................................................................................................. 55 Figure 35: A pump to clear out flooded streets of the Pikine neighbourhood of the Senegal capital
Dakar PROGEP measures (Photo Mamadou Lamine Camara, Agence de développement municipal
(ADM) Dakar) ....................................................................................................................................... 57 Figure 36: In the "streets" of Dakar, 17/09/2009 - Photo: SOS Archives ............................................. 59 Figure 37: Flooded settlement in the Department of Pikine, August/September 2012 (Photo: Steve
Cockburn) .............................................................................................................................................. 60 Figure 38: Flooding in Pikine, Sptember 2012 (Senegal7.com) ........................................................... 60 Figure 39: Coverage of the 5m Digital Terrain Model of Dakar (© BaseGéo Sénégal) ....................... 63 Figure 40: Flooded areas in southern part of Pikine (neighbourhood of Diammaguen) in August 2015
(image recorded on 27/08/2015, © Maxar Technologies)..................................................................... 64 Figure 41: Flooded areas in southern part of Pikine (neighbourhood of Dalifort) in August 2017
(image recorded on 13/08/2017, © Maxar Technologies)..................................................................... 64 Figure 42: Subset of Flood Hazard Map of Dakar (Department of Pikine) (Background Image:
Sentinel 2, recorded on 10/10/2016, European Space Agency) ............................................................ 68 Figure 43: Subset of Flood Risk Map of Dakar (Department of Pikine) (Background Image: Sentinel 2,
recorded on 10/10/2016, European Space Agency) .............................................................................. 68 Figure 44: Percentages of flood hazard zones in Dakar core city area .................................................. 69 Figure 45: Percentages of flood hazard zones in Dakar peri-urban region ........................................... 70 Figure 46: Proportion of Residential Urban Fabric in flood hazard zones in Dakar core city area ...... 70 Figure 47: Subset of map of Residential, Industrial, Commercial and Public Urban Fabric combined
with Flood Hazard Zoning in Dakar’s department of Pikine (Background Image: Sentinel 2, recorded
on 10/10/2016, European Space Agency) ............................................................................................. 71 Figure 48: Percentages of flood risk zones in Dakar Core city Area .................................................... 72 Figure 49: Percentages of flood risk zones in Dakar Larger Urban Area ............................................. 72 Figure 50: Subset of map of Residential, Industrial, Commercial and Public Urban Fabric combined
with Flood Risk Zoning in Dakar’s department of Pikine (Background Image: Sentinel 2, recorded on
10/10/2016, European Space Agency) .................................................................................................. 73 Figure 51: Proportion of Residential Urban Fabric in flood hazard zones in Dakar Core City Area .... 74 Figure 48: Satellite image showing Melaka and the surrounding area. ................................................. 81 Figure 49: Global Human Settlement Population Layer (spatial resolution of 1 km). .......................... 81 Figure 50: DLR population layer (spatial resolution of 10 m). ............................................................. 81 Figure 51: Aggregated DLR population layer (spatial resolution of 1 km). ......................................... 82
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Figure 52: High Density Core area of Melaka calculated based on the aggregated DLR population
layer. The image on the left shows the AOI overlaid on the DLR population layer. On the right, the
AOI is overlaid on a RGB satellite image. ............................................................................................ 82 Figure 53: Urban Cluster area of Melaka calculated based on the aggregated DLR population layer.. 83
List of Tables
Table 1: LU/LC Nomenclature for GPSC Cities (Core City AOI). ........................................................ 7 Table 2: LU/LC Nomenclature for GPSC Cities (Larger Urban AOI). .................................................. 7 Table 3: Number of sampling points for the Core City Area classes after applied sampling design with
information on overall land cover by class. ........................................................................................... 14 Table 4: Number of sampling points for the Larger Urban Area classes after applied sampling design
with information on overall land cover by class. .................................................................................. 14 Table 5: Accuracies exhibited by the WSF2015 according to the three considered agreement criteria
for different definitions of settlement. ................................................................................................... 19 Table 6: Acquisition dates and size of the WV2 images available for the 5 test sites analysed in the
validation exercise along with the number of corresponding 30x30m validation samples. .................. 21 Table 7: Results of the accuracy assessment of flood extents in Dakar - Overall Accuracy 90.33 %. . 24 Table 8: Detailed information on area and percentage of total area for each class for 2006 and 2018 as
well as the changes for the Core City area ............................................................................................ 32 Table 9: Overall Main LU/LC Changes Statistics for the Core City Area. ........................................... 34 Table 10: Larger Urban Area - Detailed information on area and percentage of total area for each class
for 2006 and 2018 as well as the changes. ............................................................................................ 37 Table 11: Overall LU/LC statistics of the Larger Urban Area. ............................................................. 39 Table 12: SDG 11 indicators measurable with the support of EO4SD-Urban products. ...................... 42 Table 13: Land use classes and reclassification to pre-defined damage levels in Core City Area ........ 66 Table 14: Land use classes and reclassification to pre-defined damage levels in Larger Urban Area .. 66 Table 15: Flood Hazard and Risk classification .................................................................................... 67
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List of Abbreviations
AOI Area of Interest
CDS City Development Strategy
CS Client States
DEM Digital Elevation Model
DLR German Space Agency
EDF European Development Fund
EEA European Environmental Agency
EGIS Consulting Company for Environmental Impact Assessment and Urban Planning, France
EO Earth Observation
ESA European Space Agency
EU European Union
GAF GAF AG, Geospatial Service Provider, Germany
GCC General Clauses and Conditions for ESA Contracts
GCT General Conditions of Tender
GEO Group on Earth Observations
Geo-SDI Geo Sustainable Development Indicators
GIS Geographic Information System
GISAT Geospatial Service Provider, Czech Republic
GISBOX Romanian company with activities of Photogrammetry and GIS
GPSC Global Platform for Sustainable Cities
GUF Global Urban Footprint
HR High Resolution
HRL High Resolution Layer
IFI International Financing Institute
INSPIRE Infrastructure for Spatial Information in the European Community
ISO/TC 211 Standardization of Digital Geographic Information
JR JOANNEUM Research, Austria
LU / LC Land Use / Land Cover
LULCC Land Use and Land Cover Change
MMU Minimum Mapping Unit
NDVI Normalized Difference Vegetation Index
NEO Geospatial Service Provider, The Netherlands
QA Quality Assurance
QC Quality Control
QM Quality Management
R&D Research and Development
SAR Synthetic Aperture Radar
SC Service Cluster
SIRS Geospatial Service Provider, France
SME Small and Medium-sized Enterprise
SO Service Operations
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SP Service Provider
ToC Table of Contents
UN United Nations
UNDP United Nations Development Programme
UN-ESCAP United Nations Economic and Social Commission for Asia and the Pacific
UNFCCC United Nations Framework Convention on Climate Change
UNIDO United Nations Industrial Development Organisation
UNITAR United Nations Institute for Training and Research
US United States of America
UUA User Utility Assessment
VHR Very High Resolution
WB World Bank
WBG World Bank Group
WP Work Package
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1 General Background of EO4SD-Urban
Since 2008 the European Space Agency (ESA) has worked closely together with the International
Finance Institutes (IFIs) and their client countries to harness the benefits of Earth Observation (EO) in
their operations and resources management. Earth Observation for Sustainable Development (EO4SD)
is a new ESA initiative, which aims to achieve an increase in the uptake of satellite-based information
in the regional and global IFI programmes. The EO4SD-Urban project initiated in May 2016 (with a
duration of 3 years) has the overall aim to integrate the application of satellite data for urban
development programmes being implemented by the IFIs with the developing countries. The overall
goal will be achieved via implementation of the following main objectives:
• To provide the services on a regional basis (i.e. large geographical areas); in the context of the
current proposal with a focus on S. Asia, SE Asia and Africa, for at least 35-40 cities.
• To ensure that the products and services are user-driven; i.e. priority products and services to
be agreed on with the MDBs in relation to their regional programs and furthermore to
implement the project with a strong stakeholder engagement especially in context with the
validation of the products/services on their utility.
• To provide a service portfolio of Baseline and Derived urban-related geo-spatial products that
have clear technical specifications and are produced on an operational manner that are
stringently quality controlled and validated by the user community.
• To provide a technology transfer component in the project via capacity building exercises in
the different regions in close co-operation with the MDB programmes.
This Report supports the fulfilment of the third objective, which requires the provision of geo-spatial
Baseline and Derived geo-spatial products to various stakeholders in the IFIs and counterpart City
authorities. The Report provides a Service Description, and then in Chapter 3 systematically reviews
the main production steps involved and importantly highlights whenever there are Quality Control
(QC) mechanisms involved with the related QC forms in the Annexe of this Report. The description of
the processes is kept intentionally at a top leave and avoiding technical details as the Report is
considered mainly for non-technical IFI staff and experts and City authorities. Finally, Chapter 4
presents the standard analytical work undertaken with the products which can be inputs into further
urban development assessments, modelling and reports.
2 Service Description
The following Section summarises the service as it has been realised for the city of Dakar, Senegal,
within the EO4SD-Urban Project and as it was delivered to the UNIDO (United Nations Industrial
Development Organisation), the GPSC Implementing Agency for the Senegalese city of Dakar, and
the Senegalese Governmental agencies.
2.1 Stakeholders and Requirements
The EO4SD-Urban products described in this Report were provided for the benefit of the Global
Platform for Sustainable Cities (GPSC) programme. GPSC is funded by the Global Environment
Facility (GEF) and currently includes 28 cities in 11 countries. The GPSC initiative is supported by
different Multi-Lateral Development Banks (MDBs) and UN organisations. The two Senegalese cities
of St. Louis and Dakar are part of the GPSC programme and were identified for collaboration with the
EO4SD-Urban project via interactions with UNIDO, the Implementing Agency.
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The GPSC has an overarching aim to provide a knowledge platform for partner cities, as well as
relevant networks and institutions to support the cities via:
• “Knowledge transfer activities that support urban investments and sustainability initiatives,
• A global network for collaborative engagement, tapping into and complementing existing
efforts,
• Long-term, systematic engagement with cities, financial institutions, and organizations for
transformational impact” (GPSC Programme Booklet, 2016).
Dakar is a GPSC Partner City and the capital city of Senegal. Located along the Atlantic coast in the
east of the country, this geographical position necessarily exposes the city to major environmental
hazards (flood, coastal erosion, rise of the sea level).
In the context of the GPSC programme the city has the objective to “integrate climate risks in urban
planning and management and will focus on urban planning and management, capacity building
through the development of integrated climate resilience solutions and strengthening the urban
national policy framework to promote cities’ sustainability at the national level” (GPSC website,
2018). The project also aims at developing a sustainable cities master plan.
The main local stakeholder for the city of Dakar is the Directorate of Urbanism and Architecture of the
Senegalese Ministry of Urban Renewal, Habitat and Living Environment. Other stakeholders include
mostly other Senegalese governmental agencies.
The Directorate of Urbanism and architecture, that works in close collaboration with the Ministry of
Territorial Planning, will use the EO4SD products to develop more accurate policy mechanisms.
Furthermore, the project will provide a regional view of the environmental dynamics, that will be used
by the Directorate of Urbanism and Architecture’s team.
2.2 Service Area Specification
So far, no internationally accepted definition for the term “Urban Area” and the related Core and Peri-
Urban areas exists. Different initiatives are currently trying to address a standardised approach for
defining the terms “Urban Area”. During discussions with the GPSC Co-ordinator it was considered
important to use a uniform definition for the GPSC cities in order for the cities to exchange
information and share products/experiences and conduct potential comparative studies.
In this context, it was decided to use an international approach for the demarcation of the Area of
Interest (AOI) for mapping the GPSC cities in terms of Core Urban area and Peri-Urban area. Thus,
the approach is based on the European Union’s Directorate-General for Regional and Urban Policy
(DG REGIO) method and the definitions are described in the Regional Working Paper 2014 from the
European Commission on “A harmonised definition of cities and rural areas: the new degree of
urbanisation” (European Commission, 2014). Following the naming of the DG Regio approach, the
Urban Core is named as “High Density Core” and the Peri-Urban area is termed as “Urban Cluster”.
Within the DG REGIO approach, the High Density Core area is defined as contiguous grid cells of 1
km2 with a density of at least 1 500 inhabitants per km2 and a minimum population of 50 000. The
Urban Cluster is defined as clusters of contiguous grid cells of 1 km2 with a density of at least 300
inhabitants per km2 and a minimum population of 5 000.
The DG REGIO methodology used in the EO4SD-Urban project was slightly adjusted to Non-
European countries. For the first three GPSC cities (namely Bhopal, Vijayawada and Saint-Louis)
produced within the project the Global Human Settlement Population (GHSP) grid with a spatial
resolution of 1 km were used for the classification into “High Density Core” and ”Urban Cluster”. The
raster dataset is available for the years 1975, 1990, 2000, 2015. This dataset depicts the distribution
and density of population, expressed as the number of people per cell. The data can be downloaded
under following link http://data.jrc.ec.europa.eu/dataset/jrc-ghsl-ghs_pop_gpw4_globe_r2015a.
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In 2019, a higher resolution population layer (spatial resolution of 10 m) produced by the German
Aerospace Centre (DLR) became available. The AOIs for the remaining GPSC cities (namely Melaka,
Abidjan, Dakar and Campeche) were produced based on the DLR population layer.
The High Density Core AOI for a city is created by merging the contiguous grid cells of 1 km2 with a
density of at least 1500 inhabitants per km2 and a minimum population of 50 000. In the definition of
the High Density Core the contiguity is only allowed via a vertical or horizontal connection. In a next
step, gaps are filled. Due to the coarse resolution of the population grid cells additional grid cells were
in a last step added for under estimated settlement areas. The same was done for over estimations, here
grid cells were removed. The GHSP layer can be directly used for the calculation, while the DLR
population has to be aggregated to a resolution of 1 km2 before being used for the AOI definition. In
this aggregation step, each output cell contains the sum of the input cells that are encompassed by the
extent of that new cell.
The Urban Cluster is created very similar to the High Density Core. Continuous grid cells of 1 km2
with a density of at least 300 inhabitants per km2 and a minimum population of 5 000 are merged
together to form the Urban Cluster. The contiguity within the Urban Cluster can also be diagonal.
After gaps are filled, areas, which were over or under estimated by the population grid were removed
or added to the AOI. The GHSP layer was directly used, the DLR population layer had to undergo an
aggregation step in order to reduce the spatial resolution to 1 km2.
For Bhopal and Vijayawada a buffer of 1 km was calculated around the High Density Core AOI and
the Urban Cluster AOI to smoothen the border of the AOIs.
In all remaining GPSC cities, the border was not smoothed, but when the population grid was under or
over estimating the real settlement extent, grid cells were added or removed.
In some cases, the city counterparts requested that the AOIs for the High Density Core and the Urban
Cluster follow the municipal or administrative boundary of the city. In this case, the
municipal/administrative boundary was used, but enlarged in areas where the AOI created according
to the adjusted DG Regio approach was bigger. This adjustment of the DG Regio AOI was done for
Melaka, Abidjan, Dakar and Campeche. These further adjusted DG Regio AOIs are in the following
report named as Core City Area (see Figure 1a) and Larger Urban Area (see Figure 1b).
A more detailed description on how the AOIs are calculated is provided in Annex 1.
The AOIs were presented in a power point and sent to the Users for verification. Figure 1 shows the
resulting AOIs after combining the DG Regio AOIs with the municipal/administrative boundaries of
the cities.
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Figure 1: Illustration of Core City and Larger Urban Areas of Dakar.
The Core City has an area of 422 km2 and the Larger Urban has an area of 823 km2.
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2.3 Product List and Product Specifications
During the discussions related to the AOIs the potential geo-spatial products that could be provided for
the Cities were also reviewed with the WB Team and Users. It was noted that the Baseline Land
Use/Land Cover (LU/LC) products (for the Core and Peri-Urban areas) were a standard product that
would be provided for all Cities as it is required for the derived products. In the case of Dakar, the full
list of products for both the Core and Peri-Urban areas is as follows:
• Settlement Extent & Change (producer: DLR)
• Percentage Impervious Surface & Change (producer: DLR)
• Core City and Larger Urban Land Use / Land Cover (LU/LC) & Change (producer: SIRS)
• Urban Green Areas & Change (producer: NEO)
• Flood Hazard & Risk Assessment (producer: JR)
The first two products have been generated by the German Aerospace Agency (DLR) over the full
metropolitan area for four reference years: 2000 - 2005 - 2010 - 2015.
Two time slots were used to provide historic and recent information regarding LU/LC for Dakar, 2006
and 2018 over the Core City and Larger Urban Areas. The last section of the Report is fully dedicated
to the Flood Hazard & Risk Assessment study.
2.4 Land Use/Land Cover Nomenclature
A pre-cursor to starting production was the establishment with the stakeholders on the relevant Land
Use/Land Cover (LU/LC) nomenclature as well as class definitions. The approach taken was to use a
standard remote sensing based LU/LC nomenclature i.e. the European Urban Atlas Nomenclature
(European Union, 2011) and adapt it to the User’s LU requirements. Thus, the remote-sensing based
LU/LC classes in the urban context can be grouped into five Level 1 classes, which are Artificial
Surfaces, Natural/ Semi Natural Areas, Agricultural Areas, Wetlands, and Water. These classes can
then be sub-divided into several different more detailed classes such that the dis-aggregation can be
down to Level 2-4. This hierarchical classification system is often used in operational Urban mapping
programmes and is the basis for example of the European Commission’s Urban Atlas programme
which provides pan-European comparable LU/LC data with regular updates. A depiction of the way
the levels and classes in the Urban Atlas programme are structured is presented as follows:
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Level I Artificial surfaces
- Level II: Urban Fabric
Level III
• Continuous Urban Fabric (Sealing Layer-S.L. > 80%)
• Discontinuous Urban Fabric (S.L. 10% - 80%)
Level IV
1) Discontinuous Dense Urban Fabric (S.L. 50% - 80%)
2) Discontinuous Medium Density Urban Fabric (S.L. 30% - 50%)
3) Discontinuous Low Density Urban Fabric (S.L. 10% - 30%)
4) Discontinuous Very Low Density Urban Fabric (S.L. < 10%)
- Level II: Industrial, Commercial, Public, Military, Private Units and Transport
Level III
• Industrial, Commercial, Public, Military and Private Units
• Transport Infrastructure
Level IV
5) Fast Transit Roads
6) Other Roads
7) Railway
• Port and associated land
• Airport and associated land
- Level II: Mine, Dump and Construction Sites
Level III
• Mineral Extraction and Dump Sites
• Construction Sites
• Land Without Current Use
- Level II: Artificial Non-Agricultural Vegetated Areas
Level III
• Green Urban Areas
• Sports and Leisure Facilities
(Reference: European Union, 2011)
It should be noted that in the current project, the Level 1 classes were used as the basis for
classification of the Urban Cluster areas using the High Resolution (HR) data such as Landsat or
Sentinel. However, for the High Density Core areas using the Very High Resolution (VHR) data it was
possible to go down to Level III and IV.
The different levels, classes and sub-classes from the remote sensing based urban classification, were
harmonised within the GPSC cities. The following tables give the nomenclature for the High Density Core
and the Urban Cluster region (see Table 1 and Table 2).
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Table 1: LU/LC Nomenclature for GPSC Cities (Core City AOI).
Actual and Historic Nomenclature Core City Area
Level I Level II Level III Level IV
1000 Artificial
Surfaces
1100 Residential 1110 Continuous Urban Fabric
(80 - 100 % Sealed)
1120 Discontinuous Urban
Fabric
1121 Discontinuous dense urban fabric (50 - 80 % Sealed)
1122 Discontinuous medium density urban fabric (30 - 50 % Sealed)
1123 Discontinuous low density urban fabric (10 - 30 % Sealed)
1124 Discontinuous very low density urban fabric (0 - 10 % Sealed)
1200 Industrial,
Commercial, Public,
Military, Private Units
and Transport
1210 Industrial, Commercial,
Public, Military and Private
Units
1220 Transport Infrastructure 1221 Arterial Roads
1222 Collector Roads
1223 Railway
1230 Port Area
1240 Airport
1300 Mine, Dump and
Construction Sites
1310 Mineral Extraction and
Dump Sites
1330 Construction Sites
1340 Land Without Current
Use
1400 Artificial Non-
Agricultural Vegetated
Areas
1410 Green Urban Areas
1420 Sports and Leisure
Facilities
2000 Agricultural
Area
3000 Natural and
Semi-natural Areas
3100 Forest and
Shrublands
3200 Natural Areas
(Grassland)
3300 Bare Soil
4000 Wetlands
5000 Water 5100 Inland Water
5200 Marine Water
Table 2: LU/LC Nomenclature for GPSC Cities (Larger Urban AOI).
Actual and Historic Nomenclature Larger Urban Area
Level I Level II Level III Level IV
1000 Artificial
Surfaces
2000 Agricultural
Area
3000 Natural and
Semi-natural Areas
3100 Forest and
Shrublands
3200 Natural Areas
(Grassland)
3300 Bare Soil
4000 Wetlands
5000 Water 5100 Inland Water
5200 Marine Water
It is important to note that the possibility to classify at Level IV is highly dependent on the availability
of reliable reference datasets from the City or sources such as Google Earth. This aspect is further
discussed in Chapter 3.
Especially regarding the road hierarchy used in the classification at Level IV, the international road
classification standards have been followed; this is for example defined by the European Commission
(https://ec.europa.eu/transport/road_safety/specialist/-
knowledge/road/designing_for_road_function/road_classification_en).
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Roads are divided into three groups: arterial or through traffic flow routes (in our case Arterial
Roads), distributor roads (in our case Collector Roads), and access roads (or Local Roads). The three
road types are defined as follows:
Arterial Roads:
Roads with a flow function allow efficient throughput of (long distance) motorized traffic. All
motorways and express roads as well as some urban ring roads have a flow function. The number of
access and exit points is limited. (https://ec.europa.eu/transport/road_safety/specialist/knowledge/-
road/designing_for_road_function/road_classification_en)
Collector Roads:
Roads with an area distributor function allow entering and leaving residential areas, recreational areas,
industrial zones, and rural settlements with scattered destinations. Junctions are for traffic exchange
(allowing changes in direction etc.); road sections between junctions should facilitate traffic in
flowing.
(https://ec.europa.eu/transport/road_safety/specialist/knowledge/road/designing_for_road_function/roa
d_classification_en)
Local Roads:
Roads with an access function allow actual access to properties alongside a road or street. Both
junctions and the road sections between them are for traffic exchange. (https://ec.europa.eu/transport/-
road_safety/specialist/knowledge/road/designing_for_road_function/road_classification_en).
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2.5 World Settlement Extent
Reliably outlining settlements is of high importance since an accurate characterization of their extent
is fundamental for accurately estimating, among others, the population distribution, the use of
resources (e.g. soil, energy, water, and materials), infrastructure and transport needs, socioeconomic
development, human health and food security. Moreover, monitoring the change in the extent of
settlements over time is of great support for properly modelling the temporal evolution of urbanization
and thus, better estimating future trends and implementing suitable planning strategies.
At present, no standard exists for defining settlements and worldwide almost each country applies its
own definition either based on population, administrative or geometrical criteria. The German Space
Agency (DLR) was responsible for the provision of the “Settlement Extent” product; when generating
the settlement extent maps from HR imagery, pixels are labelled as settlement if they intersect any
building, lot or – just within urbanized areas – roads and paved surface where we define:
• building as any structure having a roof supported by columns or walls and intended for the
shelter, housing, or enclosure of any individual, animal, process, equipment, goods, or
materials of any kind;
• lot as the area contained within an enclosure (wall, fence, hedge) surrounding a building or a
group of buildings. In cases where there are many concentric enclosures around a building, the
lot is considered to stop at the inner most enclosure;
• road as any long, narrow stretch with a smoothed or paved surface, made for traveling by
motor vehicle, carriage, etc., between two or more points;
• paved surface as any level horizontal surface covered with paving material (i.e., asphalt,
concrete, concrete pavers, or bricks but excluding gravel, crushed rock, and similar materials).
Instead, pixels not satisfying this condition are marked as non-settlement.
The settlement extent product is a binary mask outlining - in the given Area of Interest (AOI) –
settlements in contrast to all other land-cover classes merged together into a single information class.
The settlement class and the non-settlement class are associated with values “255” and “0”,
respectively.
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2.6 Percentage Impervious Surface
Settlement growth is associated not only to the construction of new buildings, but – more in general –
to a consistent increase of all the impervious surfaces (hence also including roads, parking lots,
squares, pavement, etc.), which do not allow water to penetrate, forcing it to run off. To effectively
map the percentage impervious surface (PIS) is then of high importance being it related to the risk of
urban floods, the urban heat island phenomenon as well as the reduction of ecological productivity.
Moreover, monitoring the change in the PIS over time is of great support for understanding, together
with information about the spatiotemporal settlement extent evolution, also more details about the type
of urbanization occurred (e.g. if areas with sparse buildings have been replaced by highly impervious
densely built-up areas or vice-versa).
In the framework of the EO4SD-Urban project, one pixel in the generated PIS maps is associated with
the estimated percentage of the corresponding surface at the ground covered by buildings or paved
surfaces, are defined as:
• building as any structure having a roof supported by columns or walls and intended for the
shelter, housing, or enclosure of any individual, animal, process, equipment, goods, or materials
of any kind;
• paved surface as any level horizontal surface covered with paving material (i.e. asphalt, concrete,
concrete pavers, or bricks but excluding gravel, crushed rock, and similar materials).
The product provides for each pixel in the considered AOI the estimated PIS. Specifically, values are
integer and range from 0 (no impervious surface in the given pixel) to 100 (completely impervious
surface in the given pixel) with step 5.
2.7 Urban Green Areas Nomenclature
Developing cities in a sustainable way implies to preserve and promote green areas also and especially
within the urban extent. Green areas refer to any surfaces covered by vegetation (grass, bushes, trees).
Table 1: Nomenclature used for the mapping and identification of Urban Green Areas.
Single date
Code 0 Non-urban green area
Code 1 Urban green area
Code 255 Non-urban areas. All areas that do not fall in “Artificial Surfaces” Level 1 class of
the Land Use Land Cover product (See Table 1).
Change product
Code 0 Non-urban green area. No vegetated surfaces occurring on “Artificial Surfaces”,
Level I, at both points in time.
Code 1 Permanent urban green area. Vegetated surfaces in historic and recent year.
Code 2 Loss of urban green area. Vegetated areas in historic year, which changed to non-
vegetated areas in recent year.
Code 3 New urban green area. Non-vegetated surfaces in historic year with vegetation
cover in recent year.
Code 255 Non-Urban Areas. All areas that do not fall in “Artificial Surfaces” Level 1 class of
the Land Use Land Cover product.
2.8 Terms of Access
The Dissemination of the digital data and the Report was undertaken via FTP.
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3 Service Operations
The following Sections present all steps of the service operations including the necessary input data,
the processing methods, the accuracy assessment and the Quality Control procedures. Methods are
presented in a top-level and standardised manner for all the EO4SD-Urban City Reports.
3.1 Source Data
This Section presents a summary of the remote sensing and ancillary datasets that were used. Different
types of data from several data providers have been acquired. A complete list of source data as well as
a quality assessment is provided in Annex 3.
High Resolution Optical EO Data
The major data sources for the current and historic mapping of LULC for Larger Urban Area,
Settlement Extent and PIS products were Landsat and Sentinel-2 data which were accessible and
downloadable free of charge.
• Landsat 7: As a source of historical data four scenes of Landsat TM 7 from the 14th of
January 2006 to 28th of March have been acquired which covers the whole area of interest.
• Sentinel-2: The most recent data coverage comprises one Sentinel-2 data set from the 27th of
February 2018. The data was downloaded and processed at Level 1C.
Very High Resolution Optical EO Data
The VHR data for the Core Urban Area mapping had to be acquired and purchased through
commercial EO Data Providers such as Airbus Defence and European Space Imaging.
It has to be noted that under the current collaboration project the VHR EO data had to be purchased
under mono-license agreements between GAF AG and the EO Data Providers. If EO data would have
to be distributed to other stakeholders, then further licences for multiple users would have to be
purchased.
The following VHR sensor data have been acquired to cover the AOI:
• Quickbird-2:
o 4 scenes from the 07 November 2005 to the 21th of December 2005 covering 99.3% of
the Core Urban AOI
• Pléiades 1-A & Pléiades 1-B:
o 3 scenes from the 1st of March 2018 to the 3rd of March 2018 covering 99.9% of the
Core Urban AOI
Detailed lists of the used EO data as well as their quality is documented in the attached Quality
Control Sheets in Annex 3.
Ancillary Data
Open Street Map (OSM) data: OSM data is freely available and generated by volunteers across the
globe. The so-called crowd sourced data is not always complete but has for the most parts of the world
valuable spatial information. Data was downloaded to complement the Transport Network layer and
further enhanced. The spatial location of the OSM based streets was used a geospatial reference.
Detailed lists of the used EA and ancillary data as well as their quality is documented in the attached
Quality Control Sheets in Annex 3.
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3.2 Processing Methods
Data processing starts at an initial stage with quality checks and verification of all incoming data. This
assessment is performed in order to guarantee the correctness of data before geometric or radiometric
pre-processing is continued. These checks follow defined procedures in order to detect anomalies,
artefacts and inconsistencies. Furthermore, all image and statistical data were visualized and
interpreted by operators.
The main techniques and standards used for data analysis, processing and modelling for each product
are described in Annex 2.
3.3 Accuracy Assessment of Map Products
Data and maps derived from remote sensing contain - like any other map - uncertainties which can be
caused by many factors. The components, which might have an influence on the quality of the maps
derived from EO include quality and suitability of satellite data, interoperability of different sensors,
radiometric and geometric processing, cartographic and thematic standards, and image interpretation
procedures, post-processing of the map products and finally the availability and quality of reference
data. However, the accuracy of map products has a major impact on secondary products and its utility
and therefore an accuracy assessment was considered as a critical component of the entire production
and products delivery process. The main goal of the thematic accuracy assessment was to guarantee
the quality of the mapping products with reference to the accuracy thresholds set by the user
requirements.
The applied accuracy assessments were based on the use of reference data and applying statistical
sampling to deduce estimates of error in the classifications. In order to provide an efficient, reliable
and robust method to implement an accuracy assessment, there are three major components that had to
be defined: the sampling design, which determines the spatial location of the reference data, the
response design that describes how the reference data is obtained and an analysis design that defines
the accuracy estimates. These steps were undertaken in a harmonized manner for the validation of all
the geo-spatial products.
3.3.1 Accuracy Assessment of the LU/LC Products
Sampling Design
The sampling design specifies the sample size, sample allocation and the reference assessment units
(i.e. pixels or image blocks). Generally, different sampling schemes can be used in collecting
accuracy assessment data including: simple random sampling, systematic sampling, stratified
random sampling, cluster sampling, and stratified systematic unaligned sampling. In the current
project a single stage stratified random sampling based on the method described by Olofson et al
(20131) was applied which used the map product as the basis for stratification. This ensured that all
classes, even very minor ones were included in the sample.
The sampling design is applied separately for the Core City Area and for the Larger Urban Area
classification.
1 Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in
land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation.
Remote Sensing of Environment, 129, 122–131. doi:10.1016/j.rse.2012.10.031
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In the complex LU/LC product with many classes, this usually results in a large number of strata
(one stratum per LU/LC classes), of which some classes cover only very small areas (e.g. sport
fields, cemeteries) and not being adequately represented in the sampling. In order to achieve a
representative sampling for the statistical analyses of the mapping accuracy it was decided to extend
the single stage stratified random sampling. Slightly different approaches were used for the Core
City and the Larger Urban Area classification.
The first step is the same for both classifications: the number of required samples is allocated within
each of the Level I strata (1000 Artificial Surfaces, 2000 Agricultural Area, 3000 Natural and Semi-
natural Areas, 4000 Wetlands, 5000 Water).
In the second step, all Level III classes that were not covered by the first sampling were grouped into
one new stratum for the Core City Area classification. For the Larger Urban Area classification all
Level II classes that were not covered by the first sampling were grouped into one new stratum.
Within that stratum the same number of samples was randomly allocated as the Level I strata
received. To avoid a clustering of point samples within classes and to minimise the effect of spatial
autocorrelation a minimum distance in between the sample points was set to be 150 m. The final
sample size for each class can be considered to be as close as possible to the proportion of the area
covered by each stratum considering that the target was to determine the overall accuracy of the
entire map.
The total sample size per stratum was determined by the expected standard error and the estimated
error rate based on the following formula, which assumes a simple random sampling (i.e. the
stratification is not considered):
n = 𝑃∗𝑞
(𝐸
𝑧)²
n = number of samples per strata / map class
p = expected accuracy
q = 1 – p
E = Level of acceptable (allowable) sample error
Z = z-value (the given level of significance)
Hence, with an expected accuracy of p = 0.85, a 95% confidence level and an acceptable sampling
error of 5%, the minimum sample size is 196. A 10% oversampling was applied to compensate for
stratification inefficiencies and potentially inadequate samples (e.g. in case of cloudy or shady
reference data). For each Level I strata 215 samples have been randomly allocated. Afterwards, for
all classes of Level III of the Core City Area classification that did not received samples in the first
run, additionally 215 samples were randomly drawn across all these classes. A summary of the
number of sample point for each Core City Area class is given in Table 3.
The same applies for the Larger Urban Area classification: All Level II classes that did not receive
samples in the first run, additionally 215 samples were randomly drawn across all these classes. A
summary of the number of sample point for each Larger Urban Area class is given in
Table 4.
The main difference of the sampling design for the two areas is that the resampling is done at Level III
for the Core City Area and at Level II for the Larger Urban Area.
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Table 3: Number of sampling points for the Core City Area classes after applied sampling design with
information on overall land cover by class.
Class Name Class ID
No. Of
Sampling
Points
Km2 Coverage
Continuous Urban Fabric 1110 196 108.2
Discontinuous Urban Fabric 1120 19 7.1
Industrial, Commercial, Public,
Military and Private Units 1210 148 31.4
Transport Infrastructure 1220 31 7.9
Port Area 1230 12 1.9
Airport 1240 24 5.5
Mineral Extraction and Dump Sites 1310 28 3.5
Construction Sites 1330 32 4.1
Land Without Current Use 1340 141 17.3
Green Urban Areas 1410 20 2.3
Sports and Leisure Facilities 1420 20 2.6
Agricultural Area 2000 180 22.3
Forest and Shrublands 3100 40 5.0
Natural Areas (Grassland) 3200 151 18.7
Bare Soil 3300 107 13.2
Wetlands 4000 59 7.3
Inland Water 5100 27 3.3
Marine Water 5200 215 160.7
Total - 1450 422.4
Table 4: Number of sampling points for the Larger Urban Area classes after applied sampling design with
information on overall land cover by class.
Class Name Class
ID
No. Of
Sampling
Points
Km2 Coverage
Artificial Surfaces 1000 215 259.9
Agriculture 2000 215 243.6
Forest and Shrublands 3100 61 15.5
Natural Areas 3200 215 54.8
Bare Soil 3300 75 29.1
Wetlands 4000 31 10.3
Inland Water 5100 27 5.8
Marine Water 5200 215 204.4
Total - 1054 823.4
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Response Design
The response design determines the reference information for comparing the map labels to the
reference labels. Collecting reference data on the ground by means of intensive fieldwork is both
costly and time-consuming and is in most projects not feasible. The most cost-effective reference data
sources are VHR satellite data with 0.5 m to 1 m spatial resolution. Czaplewski (2003)2 indicated that
visual interpretation of EO data is acceptable if the spatial resolution of EO data is sufficiently better
compared to the thematic classification system. However, if there are no EO data with better spatial
resolution available, the assessment results need to be checked against the imagery used in the
production process.
The calculated number of necessary sampling points for each mapping category was randomly
distributed among the strata and overlaid onto the two LULC mapping products. The following two
Figures (see Figure 2 and Figure 3) are showing the mapping result with the overlaid sample points.
Figure 2: Mapping result of the Core City Area of Dakar of the year 2018 overlaid with randomly
distributed sample points used for accuracy assessment.
2 Czaplewski, R. L. (2003). Chapter 5: accuracy assessment of maps of forest condition: statistical design and
methodological considerations, pp. 115–140. In Michael A.Wulder, & Steven E. Franklin (Eds.), Remote
sensing of forest environments: concepts and case studies. Boston: Kluwer Academic Publishers (515 pp.).
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Figure 3: Mapping result of the Larger Urban Area of Dakar of the year 2018 overlaid with randomly
distributed sample points used for accuracy assessment.
In this way a reference information could be extracted for each sample point by visual interpretation of
the VHR data for all mapped classes. The size of the area to be observed had to be related to the
Minimum Mapping Unit (MMU) of the map product to be assessed. The reference information of each
sampling point was compared with the mapping results and the numbers of correctly and not-correctly
classified observations were recorded for each class. From this information the specific error matrices
and statistics were computed (see next Section).
Analysis
Each class usually has errors of both omission and commission, and in most situations, these errors for
a class are not equal. In order to calculate these errors as well as the uncertainties (confidence
intervals) for the area of each class a statistically sound accuracy assessment was implemented.
The confusion matrix is a common and effective way to represent quantitative errors in a categorical
map, especially for maps derived from remote sensing data. The matrices for each assessment epoch
were generated by comparing the “reference” information of the samples with their corresponding
classes on the map. The Reference represented the “truth”, while the Map provided the data obtained
from the map result. Thematic accuracy for each class and overall accuracy is then presented in error
matrices. Unequal sampling intensity resulting from the random sampling approach was accounted for
by applying a weight factor (p) to each sample unit based on the ratio between the number of samples
and the size of the stratum considered3:
3 Selkowitz, D. J., & Stehman, S. V. (2011). Thematic accuracy of the National Land Cover Database (NLCD)
2001 land cover for Alaska. Remote Sensing of Environment, 115(6), 1401–1407.
doi:10.1016/j.rse.2011.01.020.
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�̂�𝑖𝑗 = (1
𝑀) ∑
1
𝜋𝑢ℎ∗
𝑥∈(𝑖,𝑗)
Where i and j are the columns and rows in the matrix, M is the total number of possible units
(population) and π is the sampling intensity for a given sample unit u in stratum h.
Overall accuracy and User and producer accuracy were computed for all thematic classes and 95%
confidence intervals were calculated for each accuracy metric.
The standard error of the error rate was calculated as follows: 𝜎ℎ = √𝑝ℎ(1−𝑝ℎ)
𝑛ℎ where nh is the sample
size for stratum h and ph is the expected error rate. The standard error was calculated for each stratum
and an overall standard error was calculated based on the following formula:
𝜎 = √∑ 𝑤ℎ2. 𝜎ℎ
2
In which 𝑤ℎ is the proportion of the total area covered by each stratum. The 95% Confidence Interval
(CI) is +/- 1.96*𝜎.
Results
The confusion matrices are provided within the Annex 3 and show the mapping error for each relevant
class. For each class the number of samples which are correctly and not correctly classified are listed,
this allows the calculation of the user and producer accuracies for each class as well as the confidence
interval at 95% confidence levels based on the formulae above.
The Land Use/Land Cover product for Dakar in 2018 in Core City Area has an overall mapping
accuracy of 98.94% with a CI ranging from 98.41% to 99.47% at a 95% CI.
For the Larger Urban Area, the overall accuracy is 89.23% with a CI ranging from 87.36% to
91.1% at a 95% CI. The specific class accuracies are given in Annex 3.
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3.3.2 Accuracy Assessment of the World Settlement Extent Product
In the following, the strategy designed for validating the World Settlement Extent (WSE) or World
Settlement Footprint (WSF) 2015, i.e. a global settlement extent layer obtained as a mosaic of ~18.000
tiles of 1x1 degree size where the same technique employed in the EO4SD-Urban project is presented.
In particular, specific details are given for all protocols adopted for each of the accuracy assessment
components, namely response design, sampling design, and analysis; final results are discussed
afterwards. In the light of the quality and amount of validation points considered, it can be reasonably
assumed that the corresponding quality assessment figures are also representative for any settlement
extent map generated in the framework of EO4SD-Urban.
Response Design
The response design encompasses all steps of the protocol that lead to a decision regarding agreement
of the reference and map classifications. The four major features of the response design are the source
of information used to determine the source of reference data, the spatial unit, the labelling protocol
for the reference classification, and a definition of agreement.
• Source of Reference Data: Google Earth (GE) satellite/aerial VHR imagery has been used given
its free access and the availability for all the project test sites in the period 2014-2015. In
particular, GE automatically displays the latest available data, but it allows to browse in time over
all past historical images. The spatial resolution varies depending on the specific data source; in
the case of SPOT imagery it is ~1.5m, for Digital Globe's WorldView-1/2 series, GeoEye-1, and
Airbus' Pleiades it is in the order of ~0.5m resolution, whereas for airborne data (mostly available
for North America, Europe and Japan) it is about 0.15m.
• Spatial Assessment Unit: A 3x3 block spatial assessment unit composed of 9 cells of 10x10m
size has been used. Specifically, this choice is justified one the one hand by the fact that input
data with different spatial resolutions have been used to generate the WSF2015 (i.e. 30m Landsat-
8 and 10m S1). On the other hand, GE imagery exhibited in some cases a mis-registration error of
the order of 10-15m, hence using a 3x3 block allows defining an agreement e.g. based on
statistics computed over 9 pixels, thus reducing the impact of such shift.
• Reference Labelling Protocol: For each spatial assessment block any cell is finally labelled as
settlement if it intersects any building, lot or – just within settlements – roads and paved surface.
Instead, pixels not satisfying this condition are marked as non-settlement.
• Definition of Agreement: Given the classification and the reference labels derived as described
above, three different agreement criteria have been defined:
8) for each pixel, positive agreement occurs only for matching labels between the
classification and the reference;
9) for each block, a majority rule is applied over the corresponding 9 pixels of both the
classification and the reference; if the final labels match, then the agreement is positive;
10) for the classification a majority rule is applied over each assessment block, while for the
reference each block is labelled as “settlement” only in the case it contains at least one
pixel marked as “settlement”; if the final labels match, then the agreement is positive.
Crowd-sourcing was performed internally at Google. In particular, by means of an ad-hoc tool,
operators have been iteratively prompted a given cell on top of the available Google Earth reference
VHR scene closest in time to the year 2015 and given the possibility of assigning to each cell a label
among: “building”, “lot”, “road/paved surface” and “other”. For training the operators, a
representative set of 100 reference grids was prepared in collaboration between Google and DLR.
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Sampling Design
The stratified random sampling design has been applied since it satisfies the basic accuracy assessment
objectives and most of the desirable design criteria. In particular, stratified random sampling is a
probability sampling design and it is one of the easier to implement; indeed, it involves first the
division of the population into strata within which random sampling is performed afterwards. To
include a representative population of settlement patterns, 50 out of the ~18.000 tiles of 1x1 degree
size considered in the generation of the WSF2015 have been selected based on the ratio between the
number of estimated settlements (i.e. disjoint clusters of pixels categorized as settlement in the
WSF2015) and their area. In particular, the i-th selected tile has been chosen randomly among those
whose ratio belongs to the interval ]𝑃2(𝑖−1); 𝑃2𝑖], 𝑖 ∈ [1; 50] ⊂ ℕ (where 𝑃𝑥 denotes the x-th percentile
of the ratio).
Table 5: Accuracies exhibited by the WSF2015 according to the three considered agreement criteria for
different definitions of settlement.
Settlement = Accuracy
Measure
Agreement Criterion
1 2 3
buildings
OA% 86.96 87.86 91.15
AA% 88.57 90.35 88.91
Kappa 0.6071 0.6369 0.7658
UANS% - UAS% 98.11 54.69 98.73 56.76 94.84 80.58
PANS% - PAS% 86.24 90.90 86.72 93.98 93.32 84.51
buildings + lots
OA 88.08 88.94 91.26
AA% 88.64 90.19 88.71
Kappa 0.6510 0.6784 0.7716
UANS% - UAS% 97.54 60.71 98.13 62.66 94.29 82.62
PANS% - PAS% 87.79 89.49 88.26 92.12 93.95 83.48
buildings + lots
+ roads / paved
surface
OA 88.77 90.09 88.51
AA% 86.34 88.28 84.27
Kappa 0.6938 0.7317 0.7219
UANS% - UAS% 94.49 72.20 95.35 75.06 88.13 89.60
PANS% - PAS% 90.78 81.91 91.62 84.94 96.04 72.51
As the settlement class covers a sensibly small proportion of area compared to the merger of all other
non-settlement classes (~1% of Earth’s emerged surface), an equal allocation reduces the standard
error of its class-specific accuracy. Moreover, such an approach allows to best address user’s accuracy
estimation, which corresponds to the map “reliability” and is indicative of the probability that a pixel
classified on the map actually represents the corresponding category on the ground. Accordingly, in
this framework for each of the 50 selected tiles we randomly extracted 1000 settlement and 1000 non-
settlement samples from the WSF2015 and used these as centre cells of the 3x3 reference block
assessment units to label by photointerpretation. Such a strategy resulted in an overall amount of
(1000 + 1000) × 9 × 50 = 900.000 cells labelled by the crowd.
Analysis
As measures for assessing the accuracy of the settlement extent maps, we considered:
• the percentage overall accuracy OA%;
• the Kappa coefficient;
• the percentage producer’s (PAS%, PANS%) and user’s (UAS%, UANS%) accuracies for both the
settlement and non-settlement class;
• the percentage average accuracy AA% (i.e., the average between PAS% and PANS%).
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Results
Table 5 reports the accuracies exhibited by the WSF2015 according to the three considered agreement
criteria for different definitions of settlement; specifically, we considered as “settlement” all areas
covered by: i) buildings; ii) buildings or building lots; or iii) buildings, building lots or roads / paved
surfaces. As one can notice, accuracies are always particularly high, thus confirming the effectiveness
of the employed approach and the reliability of the final settlement extent maps. The best
performances in terms of kappa are obtained when considering settlements as composed by buildings,
building lots and roads / paved surfaces for criteria 1 and 2 (i.e., 0.6938 and 0.7317, respectively) and
by buildings and building lots for criteria 3 (0.7716); the OA% follows a similar trend. This is in line
with the adopted settlement definition. Moreover, agreement criteria 3 results in accuracies
particularly high with respect to criteria 1 and 2 when considering as settlement just buildings or the
combination of buildings and lots. This can be explained by the fact that when the detection is mainly
driven by Landsat data then the whole 3x3 assessment unit tends to be labelled as settlement if a
building or a lot intersect the corresponding 30m resolution pixel.
3.3.3 Accuracy Assessment of the Percentage Impervious Surface Product
In the following section, the strategy designed for validating the PIS product is presented; specifically,
details are given for all protocols adopted for each of the accuracy assessment components, namely
response design, sampling design, and analysis. Results are discussed afterwards.
Response Design
The response design encompasses all steps of the protocol that lead to a decision regarding agreement
of the reference and map classifications. The four major features of the response design are the source
of information used to determine the source of reference data, the spatial unit, the labelling protocol
for the reference classification, and a definition of agreement.
• Source of Reference Data: Cloud-free VHR multi-spectral imagery (Visible + Near Infrared)
acquired at 2m spatial resolution (or higher) covering a portion of the AOI for which the Landsat-
based PIS product has been generated;
• Spatial Assessment Unit: A 30x30m size unit has been chosen according to the spatial resolution
of the Landsat imagery employed to generate the PIS product;
• Reference Labelling Protocol: As a first step, the NDVI is computed for each VHR scene
followed by a manual identification of the most suitable threshold that allows to exclude all the
vegetated areas (i.e. non-impervious). Then, the resulting mask is refined by extensive
photointerpretation.
• Definition of Agreement: The above-mentioned masks are aggregated at 30m spatial resolution
and compared per-pixel with the resulting VHR-based reference PIS to the corresponding portion
of the Landsat-based PIS product.
Sampling Design
The entirety of pixels covered by the available VHR imagery over the given AOI is employed for
assessing the quality of the Landsat-based PIS product.
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Analysis
As measures for assessing the accuracy of the PIS maps, following indices are computed:
• the Pearson’s Correlation coefficient: it measures the strength of the linear relationship between
two variables and it is defined as the covariance of the two variables divided by the product of
their standard deviations; in particular, it is largely employed in the literature for validating the
output of regression models;
• The Mean Error (ME): it is calculated as the difference between the estimated value (i.e., the
Landsat-based PIS) and the reference value (i.e., the VHR-based reference PIS) averaged over all
the pixels of the image;
• The Mean Absolute Error (MAE): it is calculated as the absolute difference between the estimated
value (i.e., the Landsat-based PIS) and the reference value (i.e., the VHR-based reference)
averaged over all the pixels of the image.
Results
To assess the effectiveness of the method developed to generate the PIS maps, its performances over 5
test sites is analysed (i.e. Antwerp, Helsinki, London, Madrid and Milan) by means of WorldView-2
(WV2) scenes acquired in 2013-2014 at 2m spatial resolution. In particular, given the spatial detail
offered by WV2 imagery, it was possible to delineate with a very high degree of confidence all the
buildings and other impervious surfaces included in the different investigated areas. Details about
acquisition date and size are reported in Table 6, along with the overall number of final 30x30m
validation samples derived for the validation exercise. Such a task demanded a lot of manual
interactions and transferring it to other AOIs would require extensive efforts; however, it can be
reasonably assumed that the final quality assessment figures (computed on the basis of more than 1.9
million validation samples) shall be considered representative also for PIS maps generated in the
framework of EO4SD-Urban. Table 6 reports the quantitative results of the comparison between the
PIS maps generated using Landsat-7/8 data acquired in 2013-2014 and the WV2-based reference PIS
maps. In particular, the considered approach allowed to obtain a mean correlation of 0.8271 and
average ME and MAE equal to -0.09 and 13.33, respectively, hence assessing the great effectiveness
of the Landsat-based PIS products. However, it is worth also pointing out that due to the different
acquisition geometries, WV2 and LS8 images generally exhibit a very small shift. Nevertheless,
despite limited, such displacement often results in a one-pixel shift between the Landsat-based PIS and
the WV2-based reference PIS aggregated at 30m resolution. This somehow affects the computation of
the MAE and of the correlation coefficient (which however yet resulted in highly satisfactory values).
Instead, the bias does not alter the ME, which always exhibited values close to 0, thus confirming the
capabilities of the technique and the reliability of the final products.
Table 6: Acquisition dates and size of the WV2 images available for the 5 test sites analysed in the
validation exercise along with the number of corresponding 30x30m validation samples.
Acquisition Date
[DD.MM.YYYY]
Original Size
[2x2m pixel]
Validation Samples
[30x30m unit]
Antwerp 31.07.2014 5404 x 7844 188.280
Helsinki 21.04.2014 12468 x 9323 516.882
London 28.08.2013 7992 x 8832 313.937
Madrid 20.12.2013 10094 x 13105 588.202
Milan 14.05.2014 8418 x 7957 297.330
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3.3.4 Accuracy Assessment of Urban Green Areas Product
The validation of the Green Area mapping results is done in a similar way as the validation for the
Land Use Land Cover product. The necessary amount of sampling points is calculated according to the
formula of Goodchild et al. (1994), which is given in Table 2.
Table 2: Calculation of the minimum number of samples according Goodchild et al. (1994).
Variables Values
p 0.85
q 0.15
E 0.05
z 1.96
196
n with 10% oversampling 215
with:
p = required accuracy of the data
q = 1-p
E = Level of acceptable (allowable) sample error
Z = value from table (for the given level of significance)
The calculated number of 215 sample points was randomly distributed among the entire map and
overlaid on the VHR data of each epoch. The following Figure (see Figure 4) shows the mapping
result with the overlaid sample points.
Figure 4: Result of the Urban Green Area mapping in Dakar (change product) with sampling points used
for product validation.
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At each sample point location, the reference data was collected by visual interpretation of the VHR
data. The size of the area to be observed had to be related to the Minimum Mapping Unit (MMU) of
the map product to be assessed. Finally, visual interpreted land cover type was compared with the
mapping results and the numbers of correctly and not-correctly classified observations were recorded.
From this information the specific error matrices and statistics were computed.
The confusion matrices show the mapping error for each relevant class. For each class the number of
samples which are correctly and not correctly classified are listed in the Tables below. They allow the
calculation of the user and producer accuracies for each class as well as the confidence interval at 95%
confidence levels based on the formulae above. The results of the Accuracy Assessment are listed in
Table 3 and Table 4 below, for 2006 and 2018 respectively.
Table 3: Results of the Accuracy Assessment of Urban Green Areas in Dakar, 2006.
Overall Accuracy: 99.07 %.
Urban Green 2006 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 175 2 177
1 - Urban Green Area 0 38 38
Totals 175 40 215
Table 4: Results of the Accuracy Assessment of Urban Green Areas in Dakar, 2018.
Overall Accuracy: 98.6 %.
Urban Green 2018 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 190 3 193
1 - Urban Green Area 0 22 22
Totals 190 25 215
The confusion matrices are additionally provided within the Quality Control documentation in Annex
3 and showing the mapping error for each relevant class. For each class the number of samples, which
are correctly and not correctly classified, are listed, which allows the calculation of the user and
producer accuracies for each class as well as the confidence interval at 95% confidence levels.
3.3.5 Accuracy Assessment of Flood Extent Product
The Accuracy Assessment of the Flood Extent product was performed only for the flood events for
which Google Earth VHR images are available for performing the extraction of the reference dataset
by independent visual interpretation. Sampling points are selected for each event to ensure a
representative sampling for evaluating the product accuracy. Flood event years are going from 2009 to
2018.
Single-stage stratified random sampling approach was implemented. For each event, 20 sampling
points were randomly selected within the flood extent extracted by the producer (stratum 1), and
additional 10 points within a buffer area of 200 meters (stratum 2). The samples selected for each
event and combined in a single layer are illustrated in Figure 5.
In this way, the reference information was extracted for each sample point by visual interpretation of
the VHR data made available to image analysis expert. Then, the results were compiled through
confusion matrices allowing to calculate the overall thematic accuracy for each event. The results of
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the accuracy assessment for the whole series of flood events are presented in Table 7.Table 7: Results
of the accuracy assessment of flood extents in Dakar - Overall Accuracy 90.33 %.
Figure 5: Result of the Flood extent mapping in Dakar with sampling points used for product validation.
Table 7: Results of the accuracy assessment of flood extents in Dakar - Overall Accuracy 90.33 %.
Flood Extent
Dates extracted by CAPI
Reference Data Totals
Non flooded area Flooded area
Non flooded area 78 22 100
Flooded area 7 193 200
Totals 85 215 300
The number of samples which were correctly and not correctly classified is made clearly visible,
allowing the calculation of the overall accuracy, the user and producer accuracies for each class, as
well as the confidence interval at 95% confidence level. The matrices and analysis results for the
whole series of events and for each of them are made available in Annex 3.
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3.4 Quality Control/Assurance
A detailed Quality Control and Quality Assurance (QC/QA) system has been developed which records
and documents all quality relevant processes ranging from the agreed product requirements, the
different types of input data and their quality as well as the subsequent processing and accuracy
assessment steps. The main goal of the QC/QA procedures was the verification of the completeness,
logical consistency, geometric and thematic accuracy and that metadata are following ISO standards
on geographic data quality and INSPIRE data specifications. These assessments were recorded in Data
Quality Sheets which are provided in Annex 3. The QC/QA procedures were based on an assessment
of a series of relevant data elements and processing steps which are part of the categories listed below:
• Product requirements;
• Specifications of input data: EO data, in-situ data, ancillary data;
• Data quality checks: EO data quality, in-situ data quality, ancillary data quality;
• Geometric correction, geometric accuracy, data fusion (if applicable), data processing;
• Thematic processing: classification, plausibility checks;
• Accuracy: thematic accuracy, error matrices
• Delivery checks: completeness, compliancy with requirements
After each intermediate processing step a QC/QA was performed to evaluate products appropriateness
for the subsequent processing (see Figure 6).
Figure 6: Quality Control process for EO4SD-Urban product generation. At each intermediate processing
step output properties are compared against pre-defined requirements.
After the initial definition of the product specifications (output) necessary input data were defined and
acquired. Input data include all satellite data and reference data e.g. in-situ data, reference maps,
topographic data, relevant studies, existing standards and specifications, statistics. These input data
were the baseline for the subsequent processing and therefore all input data had to be checked for
completeness, accuracy and consistency. The evaluation of the quality of input data provides
confidence of their suitability for further use (e.g. comparison with actual data) in the subsequent
processing line. Data processing towards the end-product required multiple intermediate processing
steps. To guarantee a traceable and quality assured map production the QC/QA assessment was
performed and documented by personnel responsible for the Quality Control/Assurance. The results of
all relevant steps provided information of the acceptance status of a dataset/product.
The documentation is furthermore important to provide a comprehensive and transparent summary of
each production step and the changes made to the input data. With this information the user will be
able to evaluate the provided services and products. Especially the accuracy assessment of map
products and the related error matrices are highly important to rate the quality and compare map
products from different service providers.
The finalised QC/QA forms are attached in Annex 3.
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3.5 Metadata
Metadata provides additional information about the delivered products to enable it to be better
understood. In the current project a harmonised approach to provide metadata in a standardised format
applicable to all products and end-users was adopted. Metadata are provided as XML files, compliant
to the ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation". The metadata
files have been created and validated by the GIS/IP-operator for each map product with the
Infrastructure for Spatial Information in Europe (INSPIRE) Metadata Editor available at:
http://inspire-geoportal.ec.europa.eu/editor/.
The European Community enacted a Directive in 2007 for the creation of a common geo-data
infrastructure to provide a consistent metadata scheme for geospatial services and products that could
be used not only in Europe but globally. The geospatial infrastructure called INSPIRE was built in a
close relation to existing International Organization for Standardization (ISO) standards. These are
ISO 191115, ISO 19119 and ISO 15836. The primary incentive of INSPIRE is to facilitate the use and
sharing of spatial information by providing key elements and guidelines for the creation of metadata
for geospatial products and services.
The INSPIRE Metadata provides a core set of metadata elements which are part of all the delivered
geo-spatial products to the users. Furthermore, the metadata elements provide elements that are
necessary to perform queries, store and relocate data in an efficient manner. The minimum required
information is specified in the Commission Regulation (EC) No 1205/2008 of 3 December 2008 and
contains 10 elements:
• Information on overall Product in terms of: Point of contact for product generation, date of
creation
• Identification of Product: Resource title, Abstract (a short description of product) and Locator
• Classification of Spatial Data
• Keywords (that define the product)
• Geographic information: Area Coverage of the Product
• Temporal Reference: Temporal extent; date of publication; date of last revision; date of
creation
• Quality and Validity: Lineage, spatial resolution
• Conformity: degree of conformance to specifications
• Data access constraints or Limitations
• Responsible party: contact details and role of contact group/person
These elements (not exhaustive) constitute the core information that has to be provided to meet the
minimum requirements for Metadata compliancy. Each element and its sub-categories or elements
have specific definitions; for example, in the element “Quality” there is a component called “Lineage”
which has a specific definition as follows: “a statement on process history and/or overall quality of the
spatial data set. Where appropriate it may include a statement whether the data set has been validated
or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal
validity. The value domain of this element is free text,” (INSPIRE Metadata Technical Guidelines,
2013). The detailed information on the Metadata elements and their definitions can be found in the
“INSPIRE Metadata Implementing Rules: Technical Guidelines,” (2013). Each of the EO4SD-Urban
products will be accompanied by such a descriptive metadata file. It should be noted that the internal
use of metadata in these institutions might not be established at an operational level, but the file format
(*.xml) and the web accessibility of data viewers enable for the full utility of the metadata.
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4 Analysis of Mapping Results
This Chapter presents and assesses all results that have been produced within the framework of the
current project. Especially, it provides the results of some standard analytics undertaken with these
products including the following:
• Settlement Extent – Developments from 2000, 2005, 2010 to 2015
• Land Use / Land Cover - Status and Trends between 2006 and 2018
• Urban Green Areas - Status and Change between 2006 and 2018
It is envisaged that these analytics provide information on general trends and developments in the
Core City and Larger Urban areas, which can then be further interpreted and used by Urban planners
and the City Authorities for city planning.
It should be noted that all digital data sets for these products are provided in concurrence with this City
Report with all the related metadata and Quality Control documentation.
4.1 Settlement Extent – Developments 2000, 2005, 2010 and 2015
The Urban or World Settlement Extent (WSE) product in the EO4SD-Urban project is provided by the
German Aerospace Centre (DLR) and is provided for 4 points in time; this product and its accuracy
was described in Section 2.5 and 3.3.2. In the current project, the Urban Extent product for Dakar was
first used to assess historical developments from 2000-2015 (see Figure 7 and Figure 8). Further
analysis by overlaying administrative boundaries can be performed to assess urbanisation extent
patterns based on administrative units.
Figure 7: Settlement Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in
Dakar and surrounding region.
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Figure 8: Settlement Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in
Dakar within the High Density Area.
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4.2 Land Use / Land Cover 2003/2006 and 2018
This Section presents the results of the LU/LC mapping for the Historic and Current status as well the
statistical information on the changes between these two epochs, first for the Core City Area, and then
for the Larger Urban Area.
4.2.1 LU/LC Mapping for Core City Area
The LU/LC map generated for 2018 reference year is depicted in Figure 9 for the Core City area. A
cartographic version of the map layout is provided as a pdf file in addition to the geo-spatial product.
Figure 9: Core City Area - Detailed LU/LC 2018 in Dakar
Logically, most of the area is covered by artificial surfaces, such as the ones shown in Figure 10.
Located along the Atlantic coast, the city has developed from west to east over time. The international
airport is very close to the sea on the western district while the port and large industrial zones are
located on the south coast.
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Figure 10: Core City Area - Insight on the detailed Land Use Land Cover 2018 inside the city.
Figure 11 and Figure 12 provide more detailed information on the class disaggregation and area
coverage for the epochs 2006 and 2018.
Considering that 38% of the defined area of interest is covered by the Atlantic Ocean, it is confirmed
that the land is mainly covered by artificial surfaces. Continuous residential areas, industrial and
commercial zones as well as land without current use especially in the eastern part are the main LULC
classes with transport infrastructure (including one airport and one port area). The remaining areas in
the eastern part are used for agriculture purposes or generally covered by natural areas (grassland) or
bare soil.
Urban sprawl is clearly the main trend when comparing the figures from 2006 and 2018: continuous
residential areas as well as industrial and commercial zones expanded a lot respectively from 20 to
25.6% and from 6.1 to 7.5% over the period, while agricultural areas and natural ones decreased
respectively from 11.2 to 5.1% and from 5.6 to 4.4%.
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Figure 11: Core City Area - Detailed LU/LC 2006 structure, in % (left) and km2 (right).
Figure 12: Core City Area - Detailed LU/LC 2018 structure, in % (left) and km2 (right).
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Description of LULC Changes:
In addition to the overall LU/LC classification for the two epochs it is interesting to assess the
different trends between classes over the 12 year time period. The quantitative figures for each class
are first provided in Table 8 for the Core City area to get an overview. The next Section will highlight
the LU/LC change information between the two epochs in more detail.
Table 8: Detailed information on area and percentage of total area for each class for 2006 and 2018 as well
as the changes for the Core City area
LU/LC Classes 2018 2006 Change Change per Year
sqkm % of total sqkm % of total sqkm % sqkm %
1110 - Continuous Urban Fabric (80 -
100 % Sealed) 108.3 25.63% 84.4 19.97% 23.9 28% 2.0 2.4%
1121 - Discontinuous dense urban
fabric (50 - 80 % Sealed) 6.6 1.57% 1.5 0.36% 5.1 342% 0.4 28.5%
1122 - Discontinuous medium density
urban fabric (30 - 50 % Sealed) 0.3 0.07% 0.2 0.05% 0.1 48% 0.0 4.0%
1123 - Discontinuous low density
urban fabric (10 - 30 % Sealed) 0.2 0.04% 0.1 0.02% 0.1 62% 0.0 5.2%
1124 - Discontinuous very low density
urban fabric (0 - 10 % Sealed) 0.0 0.00% 0.0 0.00% 0.0 -80% 0.0 -6.7%
1210 - Industrial, Commercial,
Public, Military and Private Units 31.5 7.46% 25.6 6.06% 5.9 23% 0.5 1.9%
1222 - Collector Roads 7.5 1.78% 4.2 1.00% 3.3 78% 0.3 6.5%
1223 - Railway 0.4 0.10% 0.4 0.10% 0.0 0% 0.0 0.0%
1230 - Port Area 1.9 0.44% 1.7 0.40% 0.1 8% 0.0 0.7%
1240 - Airport 5.5 1.30% 7.0 1.65% -1.5 -22% -0.1 -1.8%
1310 - Mineral Extraction and Dump
Sites 3.5 0.83% 3.0 0.70% 0.6 19% 0.0 1.6%
1330 - Construction Sites 4.1 0.97% 6.0 1.42% -1.9 -32% -0.2 -2.7%
1340 - Land Without Current Use 17.5 4.15% 25.1 5.95% -7.6 -30% -0.6 -2.5%
1410 - Green Urban Areas 2.3 0.55% 2.5 0.58% -0.1 -6% 0.0 -0.5%
1420 - Sports and Leisure Facilities 2.8 0.66% 3.2 0.76% -0.4 -13% 0.0 -1.1%
2000 - Agricultural Areas 21.7 5.14% 47.4 11.21% -25.6 -54% -2.1 -4.5%
3100 - Forest and Shrublands 5.9 1.39% 10.8 2.56% -5.0 -46% -0.4 -3.8%
3200 - Natural Areas (Grassland) 18.7 4.42% 23.5 5.55% -4.8 -20% -0.4 -1.7%
3300 - Bare Soil 13.0 3.07% 6.9 1.62% 6.1 89% 0.5 7.4%
4000 - Wetlands 6.7 1.59% 5.1 1.22% 1.6 31% 0.1 2.5%
5100 - Inland Water 3.3 0.78% 2.7 0.63% 0.6 23% 0.1 1.9%
5200 - Marine Water 160.7 38.05% 161.2 38.17% -0.5 0% 0.0 0.0%
Total 422.4 100% 422.4 100% - - - -
The previous analysis is confirmed. Indeed, the main changes between the two epochs come from
agriculture whose area has been reduced by more than half (from 47.4 to 21.7 sqkm), forests and
shrublands whose extent has also been halved, while natural areas have been reduced by 20% (from
23.5 to 18.7 sqkm). This decrease of agriculture and main natural LULC classes is due to urban
expansion, especially related to high-density residential areas (+28%) and industrial and commercial
activities (+23%). This partially explains also the reduction of 30% of land without current use in the
city. Finally, it is worth to highlight the slight decrease of urban green areas (-6%) and sports and
leisure facilities (-13%).
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4.2.2 Spatial Distribution of Main LU/LC Change Categories for Core
City Area
In order to better analyze the growth trends and the spatial distribution of changes, meaningful
aggregations of the Core city area LU/LC classes in both epochs were used. The following categories
were developed for the City Core Area:
• Urban Densification: Changes from lower Residential Density Class into a higher Residential
Density class;
• Urban Residential Expansion: all changes from Non-Urban Residential classes to a
Residential class;
• Other Urban Land Use Expansion: all changes from Non-Residential Urban classes to Other
Urban and Non-Urban classes.
• Urban to Agricultural or Natural/Semi-Natural Areas
• Natural or Semi-Natural to Agricultural Areas
• Agricultural to Natural or Semi-Natural Areas
• Changes within Natural and Semi-Natural Areas
The overlay analysis of these aggregated categories of the epochs 2006 and 2018 is depicted in Figure
13 and Figure 14 for the Core City area.
Figure 13: Core City Area – LU/LC change types and spatial distribution
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Figure 14: Core City Area – LU/LC Change types between 2006 and 2018 presented in % (left) and sqkm
(right).
If there is no urban densification, the city expansion is quite huge on the original urban fringe since the
phenomenon concerns two thirds of the changes that occurred between 2006 and 2018 in Dakar,
representing around 60 sqkm of new built-up areas for residential, industrial and commercial purposes
mainly. It is also worth to mention that some new urban areas in the nearest surroundings of the airport
and a new major road from west to east were built during the period. The remaining changes mostly
concern the loss of agricultural areas to natural or semi-natural ones and change dynamics within the
latter, representing respectively 15 and 11% of the total area in evolution. Table 9 summarizes these
analysis results.
Table 9: Overall Main LU/LC Changes Statistics for the Core City Area.
Change Classes Change Core City area
sqkm %
Urban Residential Expansion 32.0 36.2%
Other Urban Land Use Expansion 28.2 31.9%
Urban to Agricultural or Natural/Semi-Natural Areas 2.4 2.8%
Natural or Semi-Natural to Agricultural Areas 2.9 3.2%
Agricultural to Natural or Semi-Natural Areas 13.2 14.9%
Change within Natural and Semi-Natural Areas 9.8 11.0%
Total 88.5 100%
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4.2.3 LU/LC Mapping for Larger Urban Area
The LU/LC for 2018 is depicted in Figure 15 for the Larger Urban area. A cartographic version of the
map layout is provided as a PDF file in addition to the geo-spatial product.
Figure 15: Larger Urban area – LU/LC 2018 in Dakar.
Figure 16 gives an insight on the LU/LC on the urban fringe.
Figure 16: Larger Urban Area - Insight on the Land Use Land Cover 2018 on the urban fringe.
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Artificial areas are predominant covering the full western part of the land as well as along the south
coast. The remaining land is covered especially by agriculture and natural or semi-natural areas, while
forests, shrublands, wetlands and inland water have a very limited presence.
Figure 17 and Figure 18 provide more detailed information on the class disaggregation and area
coverage for the epochs 2006 and 2018.
Figure 17: Larger Urban Area - Detailed LU/LC 2006 structure presented in % (left) and km2 (right).
Figure 18: Larger Urban Area - Detailed LU/LC 2018 structure presented in % (left) and km2 (right).
Artificial surfaces expanded a lot during the period as they represented only 26% in 2006 but 33% in
2018. Meanwhile, the shares of agricultural land and natural areas (grassland) decreased respectively
from 35.4% to 28.6% and from 7.2% to 5.8%. The other LU/LC classes remain quite stable in terms of
both share and area.
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Description of LULC Changes:
In addition to the overall LU/LC classification for the two epochs it is interesting to assess the
different trends between classes over the 12-year time period. The quantitative figures for each class
are first provided in Table 10 for the Larger Urban area to get an overview.
Table 10: Larger Urban Area - Detailed information on area and percentage of total area for each class
for 2006 and 2018 as well as the changes.
LU/LC Classes 2018 2006 Change Change per Year
sqkm % of total sqkm % of total sqkm % sqkm %
Artificial Surfaces 272.98 33.2% 214.21 26.02% 58.78 27.4% 4.90 2.3%
Agricultural Area 235.72 28.6% 291.44 35.40% -55.72 -19.1% -4.64 -1.6%
Forest and Shrublands 18.66 2.3% 16.56 2.01% 2.10 12.7% 0.18 1.1%
Natural Areas (Grassland) 47.69 5.8% 59.37 7.21% -11.68 -19.7% -0.97 -1.6%
Bare Soil 28.23 3.4% 21.91 2.66% 6.32 28.8% 0.53 2.4%
Wetlands 9.88 1.2% 8.95 1.09% 0.93 10.4% 0.08 0.9%
Inland Water 5.80 0.7% 6.69 0.81% -0.89 -13.3% -0.07 -1.1%
Marine Water 204.42 24.8% 204.25 24.81% 0.17 0.1% 0.01 0.0%
Total 823.38 100% 823.38 100% - - - -
The result of the analysis is confirmed: Urban expansion is the main trend with nearly 60 sqkm of new
built-up areas (+27.4%), mainly to the detriment of agricultural land in the same area proportions (-
19.1%) and to a lesser extent natural and semi-natural areas (-19.7%). It is finally worth to mention
that bare soil is expanding too over the period (+28.8%).
4.2.4 Spatial Distribution of Main LU/LC Change Categories for Larger
Urban Area
In order to better analyze the trends and the spatial distribution of changes, meaningful aggregations of
the Larger Urban Area LU/LC classes in both epochs were used. The following categories were
developed:
• Artificial Surface Expansion: all changes from non-artificial to artificial surface
• Agriculture Development: all changes from non-agricultural to agricultural land
• Changes within Natural and Semi-Natural Areas: all changes in between the natural and semi-
natural classes (e.g. Forest to Natural Grassland).
The overlay analysis of these aggregated categories of the epochs 2006 and 2018 is depicted in Figure
19 and Figure 20 for the Larger Urban Area. Table 11 summarizes the analysis results.
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Figure 19: Larger Urban Area – LU/LC Change types and spatial distribution.
Figure 20: Larger Urban Area – LU/LC Change types 2006 -2018 area in % (left) and sqkm (right)
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Not surprisingly, LU/LC changes spread over the eastern part of the area of interest, at the urban
fringe, and artificial surface expansion is the main trend over the period, representing 60% of the total
area in evolution and nearly 60 sqkm. However, it is interesting to point out that more than 30% of the
change areas concern land newly used for agriculture purposes. This trend seems clearly not to be
sufficient to compensate for the loss of agricultural land due to urban expansion, since Table 10
revealed a net decline in agriculture of nearly 20%, or 56 sqkm. Finally, changes within natural and
semi-natural areas represent 9.5% of the total area in evolution, or less than 10 sqkm, but those LU/LC
classes have a much lower coverage of the Larger Urban Area.
Table 11: Overall LU/LC statistics of the Larger Urban Area.
Change Classes Change Larger Urban
area
sqkm %
Artificial Surface Expansion 30.95 30.7%
Agriculture development 60.37 59.8%
Change within Natural and Semi-Natural Areas 9.55 9.5%
Total 100.87 100%
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4.3 Urban Green Areas
Urban green areas refer to land within and on the edges of a city that is partly or completely covered
with grass, trees, shrubs, or other vegetation. The product delivered provides accurate information (1
m resolution) on the spatial location and extent of green areas located within the urban extent (Level I
class: 1000 – Artificial Surfaces) derived from the baseline LU/LC information product. This section
will present the results of the urban green areas mapping for 2006 and 2018 focusing on the spatial and
statistical information related to the changes between these two epochs.
The Urban Green Areas change map generated over the AOI is depicted in Figure 21. A cartographic
version of the map layout is provided as a pdf file in addition to the geo-spatial product.
Figure 21: Urban Green Areas changes and spatial distribution.
The urban extent over the AOI is limited to the areas along the coastline. Clearly, the predominant
class is non-urban green area, since it represents the 69.84% of the total area, it means 136.07 sqkm.
Even so, most of green areas are located in the west of the urban extent. It also seems that there are
more variations of green areas than permanent ones over the time.
The quantitative results are shown in Figure 22 and Figure 23. The permanent green spaces over the
period represent only 3.52% of the entire area, or 6.87 sqkm. The loss of green areas represents 5.77%
while new ones only 2.48%. This difference between loss and gain is not so abrupt nevertheless, as
there is a loss of 6.40 sqkm over the time.
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Figure 22: Status and change of urban green areas in-between 2004/2006 and 2018 expressed in %.
Figure 23: Status and change of urban green areas in-between 2004/2006 and 2018 expressed in area.
69.84%
3.52%
5.77%
2.48%
18.39%
Urban Green Area Change Dakar
Non-Urban Green Area
Permanent Urban Green Area
Loss of Urban Green Area
New Urban Green Area
No change analysis due toabsence of historic VHR or out ofurban extent
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
Non-Urban GreenArea
Permanent UrbanGreen Area
Loss of UrbanGreen Area
New Urban GreenArea
No change analysisdue to absence ofhistoric VHR or out
of urban extent
km2
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4.4 Sustainable Development Goal 11 Indicators
A main objective of the EO4SD-Urban Product Portfolio is to support the reporting requirements of
Urban Development Policies and Strategies. One of the most important policy frameworks that
countries are trying to implement are the UN Sustainable Development Goals (SDGs). Seventeen
SDGs were developed with a focus on “ending extreme poverty; fighting inequality & injustice; and
addressing climate change,” by 2030. To achieve the 17 goals there are 169 targets and for each target,
indicators will be used to assess the level of achievement of the countries.
The SDG Goal 11 “Make cities and human settlements inclusive, safe, resilient and sustainable” is
specifically dedicated to Sustainable Urban Development. A list of Urban Sustainability Indicators
specific to the SDG Goal 11, have been defined in March 2016 by the UN and are described in the
UN-Habitat “SDG Goal 11 Monitoring Framework Report (UN, 2016a)”.
The EO4SD-Urban project supports seven GPSC cities, namely Bhopal and Vijayawada in India,
Campeche in Mexico, Saint-Louis and Dakar in Senegal, Abidjan in Ivory Coast and Lima in Peru.
For these seven cities, the indicators for which the needed input data is available were calculated and
are described in the following subsections. The EO4SD-Urban products can be fully or partly used for
the calculation of four SDG 11 indicators (see Table 12).
Table 12: SDG 11 indicators measurable with the support of EO4SD-Urban products.
TARGETS INDICATORS
Target 11.1: By 2030, ensure access for all to adequate,
safe and affordable housing and basic services and
upgrade slums
11.1.1: Proportion of urban population living in
slums, informal settlements or inadequate
housing
Target 11.2: By 2030, provide access to safe, affordable,
accessible and sustainable transport systems for all,
improving road safety, notably by expanding public
transport, with special attention to the needs of those in
vulnerable situations, women, children, persons with
disabilities and older persons
11.2.1: Proportion of the population that has
convenient access to public transport by sex,
age and persons with disabilities
Target 11.3: By 2030, enhance inclusive and sustainable
urbanization and capacity for participatory, integrated and
sustainable human settlement planning and management in
all countries
11.3.1: Ratio of land consumption rate to
population growth rate
Target 11.7: By 2030, provide universal access to safe,
inclusive and accessible, green and public spaces, in
particular for women and children, older persons and
persons with disabilities
11.7.1: Average share of the built-up area of
cities that is open space for public use for all, by
sex, age and persons with disabilities
A short description of the calculation as well as the needed input data and the achieved outputs are
described in the next sections for the indicators 11.2.1, 11.3.1 and 11.7.1. For Dakar, it is not possible
to calculate the Indicator 11.1.1, as the needed input data is not available.
More information including the exact calculation steps of each indicator are described in the UN-
Habitat Methodological Guidance document to monitor and report on the SDG Goal 11 indicators
(UN-Habitat, 2016).
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4.4.1 SDG 11 Indicator 11.2.1
The 11.2.1 Indicator calculates the Proportion of the population that has convenient access to public
transport by sex, age and persons with disabilities and describes the Target 11.2: “By 2030, provide
access to safe, affordable, accessible and sustainable transport systems for all, improving road safety,
notably by expanding public transport, with special attention to the needs of those in vulnerable
situations, women, and children, persons with disabilities and older persons.”
The indicator aims to monitor the use and access of public transportation system and move towards
reaching a convenient access for all. According to UN-Habitat and described in the Methodological
Guidance document (UN-Habitat, 2016) the access to public transport is considered convenient when
an officially recognised stop is accessible within a distance of 0.5 km from a reference point such as
home, school, workplace, market, etc.
The indicator is calculated by using the following formula:
% with access to public transport = 100x (population with convenient access to public transport )
city population
At a diagnosis phase, this indicator helps urban planners in identifying areas that are underserved and
to be put as a priority in the Master Plans for the localisation of transport stations and addition of new
transport lines (bus, metro, tramway, train).
Calculating this indicator considering parameters such as sex, age and persons with disabilities would
require additional census data, as not available through EO data. However, the indicator can be
calculated over the Larger Urban Area using the Global Human Settlement Population Layer and the
OpenStreetMap (OSM) transportation features (bus and subway stations and stops, railway stations,
ferry terminals), both available for the reference year 2015. It provides a first good estimate of the
proportion of the population that has convenient access to public transport.
The results are presented in Figure 24 below. For comparative reasons the graphic shows the indicator
results for all GPSC cities, but Bhopal and Vijayawada. The proportion of the population that has
convenient access to public transport is estimated more than 50% of the total population of Dakar
which is a slightly higher value than for Lima, while this indicator is lower for Abidjan and Saint-
Louis with a value close to 30% and for Campeche with 20%.
Figure 24: Proportion of population with convenient access to public transport.
31.0
19.8
53.1
31.0
47.8
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Abidjan Campeche Dakar Saint-Louis Lima
%
SDG Indicator 11.2.1: Proportion of Population with convenient access to Public Transport
(current date)
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4.4.2 SDG 11 Indicator 11.3.1
The 11.3.1 Indicator calculates the Ratio of land consumption rate to population growth rate and
describes the Target 11.3: “By 2030, enhance inclusive and sustainable urbanization and capacity for
participatory, integrated and sustainable human settlement planning and management in all countries.”
The indicator needs the definition of the two components population growth and land consumption
rate. According to the UN-Habitat Methodological Guidance document (UN-Habitat, 2016) the
population growth rate (PGR) is the increase of population in a country during a specific period,
usually one year. The PGR is expressed as a percentage of the population at the start of that period.
Further, the land consumption rate includes a) the expansion of build-up area that can be directly
measured and b) the absolute extent of land that is subject to exploitation by agriculture, forestry or
other economic activities and c) the over-intensive exploitation of land that is used for agriculture and
forestry.
The indicator is calculated by using following formula:
Ratio of land consumption rate to population growth rate (LCRPGR) = Land consumption rate
Annual population growth rate
The ratio of land consumption rate to population growth rate is an indicator for measuring land use
efficiently and is intended to answer the questions of whether the remaining undeveloped urban land is
being developed at a rate that is less than or greater than the prevailing rate of population growth. As
the ratio of land consumption rate to population growth rate is dimensionless and not straightforward
in its interpretation, several countries report the urban expansion and the population growth rate in
terms of percentage change instead of using the ratio values (Nicolau et. al., 2018).
In the following, the ratio (see Figure 25) and the percentage change values (see Figure 26) for all
GPSC cities were calculated. For the calculation of the population growth rate the Global Human
Settlement Population Layer available for the years 2000 and 2015 were used. For the calculation of
the land consumption rate the built-up area extracted from the Larger Urban Area LU/LC
classification is taken by dissolving all artificial classes.
Figure 25: Ratio of land consumption rate to population growth rate between 2005 and 2015.
1.11.0
1.5
0.7
1.1
0.6
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Bhopal Vijayawada Saint-Louis Dakar Abidjan Campeche Lima
SDG Indicator 11.3.1: Ratio of Land Consumption Rate to Population Growth Rate
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Figure 25 shows the ratio of land consumption rate to population growth rate for all GPSC cities. All
GPSC cities are visualised in the bar chart for comparative reasons. Dakar, as well as Campeche and
Lima, has a value significantly below one, which means that the population growth rate is higher than
the land consumption rate and let assume that the land is efficiently used.
Cities with values close to one have a population growth rate similar to the land consumption rate.
This indicates that the land is efficiently used too.
On the contrary, cities with values significantly above one have a higher land consumption rate than a
population growth rate. This indicates that the land is not as efficiently used as for example in Dakar.
European countries, for comparison very often have values below zero. This means that either the
population or the land consumption shows a decrease.
Looking at the percentage change values of population and land consumption between 2000/2006 and
2015/2018 all cities have a growing population and a growing urban extent, which is typical for cities
in developing countries. Dakar’s population grew by 53.2% between 2000 and 2015. Its land
consumption grew by 27.9% between 2006 and 2018. This means that the population grew faster than
the built-up area of the city and indicates also that the city seems to grow in a compact way.
In Saint-Louis for example, it is the other way around. Here the land consumption grew by 21% while
the population grew by only 17%, indicating that the city had a less compact growth in the last years.
Figure 26: Percentage change of population and land consumption between 2005 and 2015.
A significant limitation of this indicator is that the approach captures only the urban extent change, not
the internal city dynamics.
21.3
27.9
15.5
34.6
45.6
10.7
6.6
17.0
53.2
16.1
41.0
48.1
21.9
34.0
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Saint-Louis
Dakar
Vijayawada
Bhopal
Abidjan
Campeche
Lima
Percentage [%]
Percentage Change of Population and Land Consumption
Population Growth Rate (percentual increase since 2005)
Land Consumption Rate (percentual increase since 2005)
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4.4.3 SDG 11 Indicator 11.7.1
The SDG 11 Indicator 11.7.1 “Average share of the built-up area of cities that is open space for
public use for all, by sex, age and persons with disabilities” refers to the Target 11.7.: By 2030,
provide universal access to safe, inclusive and accessible, green and public spaces, in particular for
women and children, older persons and persons with disabilities.
The indicator aims to monitor the amount of land that is dedicated by cities for public space.
According to the UN-Habitat Methodological Guidance document (UN-Habitat, 2016) public space
includes open spaces and streets and should be accessible by all.
The indicator is calculated by using following formula:
% of land that is dedicated by cities for public space (open spaces and streets) =
(Total surface of open public space + Total surface of land allocated to streets)
Total surface of built up area of the urban agglomeration
The share of land in public open spaces cannot be obtained directly from the use of high-resolution
satellite imagery, because it is not possible to determine the ownership or use of open spaces by
remote sensing. Additional metadata that helps to describe the land use patterns in the locale is
additionally required to map out land that is for public and non-public use.
As this information is not available, the LU/LC classes Urban Green Areas and Sports and Leisure
Facilities, which are available in the Core City Area LU/LC classification, were taken with the
assumption that these places are public places and accessible by all.
To calculate the total surface of land allocated to streets, the road network was used. Different buffers
were applied for three different road types (6m for Arterial Roads, 5m for Collector Roads and 3m for
Local Roads) to assess the total surface of streets. The total surface of built-up area of the urban
agglomeration is extracted from the LU/LC classification by summarising all artificial classes of the
Core City Area.
The results are presented in Figure 27 below. For comparative reasons the graphic shows the indicator
results for all GPSC cities, but Lima.
The indicator was calculated for two points in time i.e. around 2005 and 2015. In Dakar, the average
share of built-up area that is open space for public use slightly increases from 17% in 2006 to 18.4% in
2018. Abidjan also shows an increase, while all the other cities show a decrease in open spaces.
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Figure 27: Average share of the built-up area that is open space for public use.
4.5 Concluding Points
This Chapter 4 presented a summary and an overview of what is possible in term of analytics with the
geo-spatial datasets provided for Dakar in the current project. This Report is a living document and
will be complemented with further analysis during the project.
28.9 27.8
11.6
28.1
17.0 17.624.1
20.8
12.2
24.518.4
14.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Bhopal Vijayawada Abidjan Campeche Dakar Saint-Louis
[%]
SDG Indicator 11.7.1: Average Share of the Built-up Area that is Open Space for Public Use
Average share of the built-up area that isopen space for public use ~ 2005 [%]
Average share of the built-up area that is open space for public use ~ 2015 [%]
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5 Flood Hazard and Risk Assessment
Flood Hazard and Risk Mapping is a vital component for appropriate land use planning in flood-prone
areas. First of all, Flood Hazard and Risk Maps are designed to increase awareness of the likelihood of
flooding among the public, local authorities and other organisations.
Specific flood regimes and underlying causes for the flooding events in the area of interest have to be
analysed carefully, as these can be very varied in different regions.
For the urban and peri-urban area of Dakar, basically two main flood scenarios have to be considered:
a. fluvial floods (seasonal floods from small rivers) after heavy tropical rains
b. floods triggered by rainfall stagnation after heavy local cloudbursts
For Dakar, the potential flood season covers the period between August and November (Diouf et al.
2013, JICA 2016); Scenario a.) and b.) normally occur at the same time.
Flooding is one of the most severe hazards threatening Dakar, and in the last years it has become a
frequent and enduring reality. The underlying causes are complex and involve not only the recent
increase of rainfalls, but in particular the whole socio-economic process of an out-of-control urban
sprawl. Almost each year between 100,000 and 300,000 people are affected by floods (Wang et al
2009, Hungerford et al. 2019).
According to (among others) Mbow et al. (2008), these floods are often connected with problems with
the drainage system. The ground water table in many regions is very shallow and Dakars drainage
system is not yet adequate to cope with heavy rain. Furthermore, rubbish is often blocking the drains,
and debris and trash clog the outlets (cf. Figure 28).
Figure 28: Four days after a storm in August 2015, flood waters are still visible in N'Gor Village, Dakar,
Senegal (Photo: Jürgen Fauth, BRACED)
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5.1 General Characteristics of the Study Area
Dakar is the capital and outstanding urban pole in Senegal. It serves as the political and administrative
centre of Senegal, as well as the international gateway for global trade and business activities.
Administratively, the region of Dakar is divided into four departments (Dakar, Guédiawaye, Pikine,
and Rufisque, cf. Figure 29) and 10 districts (Ndiaye et al. 2016). The entire urban area covers around
550 sqkm while the population in 2013 reached about 3.137,000.
Figure 29: Position of main parts of Dakar (taken from Wikipedia)
The area of interest (Service Area) as defined by the user includes substantial additional areas covered
by the sea and therefore covers a total of 823,38 sqkm (422,40 sqkm defined as Core City and 400,98
sqkm defined as Larger Urban Area, cf. Figure 30).
The former traditional villages of Rufisque, Yoff, Ngor, Ouakam, Thiaroye, Yeumbeul, Mbao,
Kounoune, etc. nowadays have become part of the urban area (Mbow et al 2008).
Historically, Dakar has been continuously expanding eastwards, receiving population influxes from
the rural areas. The rate of growth was especially rapid after Senegal became an independent state in
1960. Several very dry decades between 1968–1997 with extreme droughts resulted in massive
migration of rural people into Dakar, especially into its lowland areas, which originally were not
inhabitable. Uncontrolled urban sprawl has created an unbalanced urban structure with a concentration
of business and commercial functions in downtown Dakar. The development of infrastructures and
public facilities has not been fast enough to catch up with the rapid urbanization. The natural
condition of Dakar surrounded by the sea on three sides has limited the effects of the government’s
efforts to solve this problem. As a result, the lag has led to the deterioration of people’s living
environments and has caused huge disparities in the social services and urban services (JICA 2016).
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Figure 30: Dakar, Senegal – Service Area: pink: Core City Area of Interest; green: Larger Urban Area of
Interest (Background Image: Sentinel 2, recorded on 10/10/2016, European Space Agency)
Dakar is located on a peninsula (“Cap Vert”) that can be divided into four geomorphological zones
(JICA 2016, cf. Figure 31):
• The rocky tip at the western part of the city is formed almost entirely of Early Quaternary
dolerites and basanites (Roger et al. 2009). The volcanic rocks build hills (the “Mamelles”)
which reach a maximum height of about 100 m. The west coast between Cap Vert and Cap
Manuel, the “Corniche Ouest”, is often a rugged steep coast of volcanic rock.
• To the east, the central dune system follows that is generally aligned parallel to the coast and
running NE-SW. The dunes closer to the shore are more pronounced. Inland they become
gently undulating with the inter-dune areas infilled with colluvium. The inter-dune
depressions are known as the Niayes. They are often marshy during the rainy season and
contain relatively lush vegetation when compared with the adjacent plain. The dune systems
of Pikine, Keur Massar, Bambilor and Sangalkam stretch to the north and form a zone of
semi-fixed coastal sand dunes alternating with marine sands and brackish deposits (organic
clay and sand, gravels, Roger et al. 2009). Soils are characterized by sandy dune soils on hills
and hydromorphic or even partially halomorphic clay soils where the static groundwater table
is shallow (Mbow et al. 2008).
• The Bargny Plateau lies to the southeast of the dunes. This marly-limestone plateau extends
along the “Petite-Côte” between Rufisque, Bargny and Sendou.
• The sandstone hills are located in the far southeast of the Service Area and are a series of
Maastrichtian sandstone hills with gentle slopes up to 50 m in elevation.
No major rivers cross the Service Area. Some smaller watercourses can be found near the southern
coast (e.g. Rivière Nougouna in Rufisque) and in the southern part of the peri-urban area (e.g. near
Siendou). Unplanned urban growth and soil sealing has provoked the disappearance of most of the
natural hydrographic network in the area of the Niayes (JICA 2016).
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Figure 31: Altitudes in Dakar Service Area as derived from available Digital Terrain and Surface Models
(western part: 5m Digital Terrain Model of Dakar (BaseGéo Sénégal, (http://www.basegeo.gouv.sn/) based
on Urban Database (UDB) product; eastern part: ALOS Global Digital Surface Model "ALOS World 3D
- 30m (AW3D30)", version 2.1 (©JAXA): Amthyst areas indicate most flood prone zones (altitudes below
5 m).
Senegal’s climate is generally characterized as tropical with heat throughout the year, and possessing
well-defined dry and rainy seasons. The dry season (November to May) is dominated by the
“Harmattan” wind that brings hot and dry weather from the northeast. The rainy season is caused by
the southwest wind from the Gulf of Guinea (JICA 2016).
Dakar Region has a fairly mild climate compared to the rest of the country, due to the oceanic
influences of winds for most of the year. This climate is marked by a rainy season from June to
October which is characterized by heat, humidity and storms with heavy cloudbursts. Precipitation
peaks can be observed in August with up to 250 mm. The dry season is characterized by maritime
winds from the Azores high. These winds give rise to an unusually cool climate. During the dry season
there is almost no rain at all.
Annual rainfall has varied between 150 mm (1983) and 664 mm (2005) over recent decades. The
current average annual rainfall amounts to 484 mm and has become slightly above the mean of 410
mm between 1961-1990 (Diouf et al. 2013).
According to ANACIM (Agence Nationale de l'Aviation Civile et de la Météorologie), most heavy
rainfall events in the rainy season are caused by moving cumulonimbus system from east to west. The
rainfall’s intensity is strong, but the duration is usually short (JICA 2016).
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Figure 32: Climate average from 2000 to 2012 in Dakar, Source: World Weather Online
Beginning with the new upsurge in rainfall in 2005, and in particular in 2009 and 2012, Dakar suffered
heavy flooding, sometimes in areas that had never been flooded before. Flooding has appeared as a
major threat especially for poor population leaving in the suburbs of Dakar. The flooding process is
not a mere climate variability related issue, it is tightly bound with poor urban management and
occupation of irregular, unsuited land (Ndiaye et al. 2016).
According to Ndiaye et al. (2016), approximately 50% of the Dakar region are vulnerable to flooding
and particularly the suburban area concerning the departments of Pikine and Guédiawaye.
The vulnerability of Dakar and its inhabitants is characterized by the significant land use change
triggered by extreme droughts in the 1970s, 80s and 90s which forced rural populations into urban
areas. Today, almost half of Senegal’s population lives in urban areas (GFDRR 2014). The urban
growth of Dakar is estimated to 7 - 8% per annum since the seventies with most of the settlements
unplanned. Poor people were forced to settle in cheap yet risky lands. The most accessible land for
housing was the depressions (the “Niayes”), mostly situated in the departments of Guédiawaye and
Pikine, which were dried during the drought period (Mbow et al. 2008).
When migrants came to Pikine, they often settled in areas that were formerly wetlands but had turned
into dry patches suitable for settlement. The 1970s and 1980s were dry climatic periods in Senegal, so
the low-lying areas that constitute much of Pikine were dry, and water tables were below average
depths. As ground cover has changed from wetland or vegetation to densely populated, largely
unplanned settlements, soil compaction and drainage have become major issues in the region.
This lack of natural drainage and infiltration is exacerbated by the lack of infrastructure to facilitate
the evacuation of surface water. So far, although some networked infrastructure is under construction,
the largest infrastructure project is the construction of catchment basins across Pikine city. These
basins were placed in low-lying areas of the commune as a collection point for surface waters. Before
the construction of the basins, these areas were some of the worst flooded neighborhoods, and thus the
houses were razed, and basins created in their place. These basins have alleviated some, but certainly
not all, of the localized flooding. According to Hungerford et al. (2019), Pikine still experiences
regular and devastating floods, and residents cope with heavy economic and health burdens. One-third
of Pikine’s 1.2 million residents regularly experience flooding, with a significant portion of these
people living in areas that were not flooded previously.
The main particularity of the Service Area with regard to flood risk thus is the occupation of the
Niayes (interdunal depressions) which before were occupied by marshes, open water and natural
vegetation (Ndiaye et al. 2016).
The Niayes constitute catchment and storage areas for runoff. By building new houses in these areas,
the buildings also reduced the drainage area of the watershed. Floods result from the reduction of
infiltration of rain water in urban areas following the increase of more or less sealed surfaces (Diop et
al. 2017).
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Furthermore, there is a drastic lack of sanitation system with waste water directly released in the rest
of depressions (sludge traps) or individual pits. The aquifer in the Quaternary sands (Thiaroye aquifer)
plays a major role in supplying drinking and irrigation water. The use of this resource began in the
1950s. During the last two decades, the pumping was considerably reduced because of the nitrate
pollution derived from improper sanitation system in the urbanized area (JICA 2016). This reduction
obviously influences the flooding phenomenon as the water table has risen very close to the surface
(Mbow et al 2008, Diouf et al. 2013).
Mbow et al. (2008) differ between natural and human causes for flooding:
The natural factors are twofold: the topography and the rainfall variability. Large parts in the
departments of Pikine and Guédiawaye are characterized by depressions (Figure DTM). Floods
preferably can be observed in sites below 5 m. The groundwater table generally is near surface in these
depressions (< 1 - 2 m). The high altitudes are about 10 - 15 m on sand dunes where the original
villages were built. The fact of setting houses in normally flooded depression is one aspects of the
vulnerability of these settlements.
Another natural factor is the extreme climate variability. One aspect of climate variability is extreme
droughts during which the depressions in Dakar get dry and become a possible settlement site for most
of the poor migrants from rural yet unproductive lands. The strong rainfall years are associated with
rapid water saturation of occupied depressions.
According to Sakho (2006), the floods are not due to total rainfall alone; the most relevant rainfall
parameter is the amount of rain during the peak season in august. These heavy rains within a short
time are the main cause of flooding. The total rainfall contributes to the underground saturation whilst
heavy rains within a short time are the trigger of water accumulation in occupied depressions.
Examples for heavy rain events in the flood season of 2005 are:
• 16th-17th August : 87.4 mm
• 19th-22nd August : 184.5 mm
• 28th-31st August : 55.2 mm
However, daily maximums appear stable, although there is a trend towards increasing intensity of
rainfall for durations of up to 15 minutes (Cissé & Sèye 2015).
The most important factor for floods is the human factors with the poor management of land and the
occupation of depressions where no space was left for rainwater infiltration leading to a strong runoff
which favors rapid flooding situations. A network of roads was built inside former watercourses
without any drainage system (cf. Figure 33).
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Figure 33: Tally Neitty Mbar (main street of Djeddah Thiaroye Kao in the Department of Pikine),
flooded in 2009. Source: Requalification des zones inondés de Djeddah Thiaroye Kao.urbaDTK.org
Additionally, people release their wastes in depressions with a serious clogging process. In many
places, the duration of inundation was more than three days, which is much longer than the usual
storm events. Once the depressions are filled with floodwater, its existence is prolonged because there
is no drainage to remove the water (JICA 2016). These lasting water bodies are polluted by pathogenic
bacteria of fecal origin, even in case of low-intensity precipitation (Green Climate Fund).
According to Ndiaye et al. (2016), very low vulnerability to flooding is recorded exclusively in the
department of Grand Dakar (western part of the peninsula, downtown area).
Within the frame of the “Project for Urban Master Plan of Dakar and the Neighboring Area for 2035”
the Japan International Cooperation Agency (JICA) estimated possible flooding areas in the new
development areas (peri-urban area), by employing the Flo2D model. The grid size of the simulation is
50 m. The result is shown in Figure 34.
Based on this simulation, it is expected that excess runoff will increase in the new urban expansion
area (e.g. area of Diamniadio). This effect should be taken into account during planning for the
territory in the 2035 Master Plan. Special attention should be paid to changes in the hydrological
situation around existing small-scale reservoirs in the new urban expansion area, because of the
potential risk of spilled floodwater, as well as dam breakage.
Flood Hazard in the new development area could not be confirmed neither based on RS data nor based
on existing reports. Therefore, no hazard and risk zones were indicated on the Flood Hazard and Risk
Maps.
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Figure 34: Possible Flooding Area in the New Urban Expansion Area based on Flo2D modelling results
(taken from JICA 2016)
Regarding the situation of coastal erosion in the Service Area, according to JICA 2016, there seems to
be relatively low risk of coastal erosion at the moment in general. Coastal erosion is particularly felt in
the area of Rufisque- Bargny with the narrowing of the beach of Rufisque, particularly along the
center of the city. Damages have been observed at places where buildings were constructed along the
shoreline. Some countermeasures have been applied in Rufisque. In the Dakar Corniche area,
significant cracking of the cliffs and the degradation of coastal rocky cliffs can be observed.
On the basis of the tidal data from the UHSLC from 1996 to 2013 (with some gaps), the average
spring high tide is about 0.81 m above MSL. The World Bank (2013) estimated the premium values of
the extreme high tide event with a 100-year return period at 1.0 m for Grande Côte and 0.7 m for
Petite Côte. Considering the premium values, the extreme high tide with a 100-year return period
could be 1.81 m above MSL for Grande Côte and 1.51 m above MSL for Petite Côte. The land where
the elevation is lower than for these extreme high-tide levels could be flooded during extreme storm
events with a 100-year return period.
The future possible sea level rise due to climate change may increase the potential coastal flooding
area. The estimated increase of the mean sea level according to the World Bank (2013) is 0.2 m in
2030 and 0.8 m in 2080. There is a possibility of coastal flooding at lowland areas along the shoreline
during extreme storm events.
The magnitude of these coastal hazards could increase in the future due to climate change. There is
also a pressure resulting from development along the shoreline due to urban expansion, which raises
the risk of disaster. The proper management and conservation of the coastal area is required (Wang et
al. 2009, JICA 2016).
Flood Hazard caused by coastal erosion could not be confirmed neither based on RS data nor based on
existing reports. Therefore, no hazard and risk zones were indicated on the Flood Hazard and Risk
Maps.
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In the last years there have been multiple interventions in the suburbs of Dakar to respond to flooding.
They can be divided into two categories:
1. Structural approaches that favor physical intervention through reconstruction and engineering
works.
2. Non-structural approaches that support capacity building at the local level and social reform.
Flood management nowadays is a major part of the Senegalese government’s Disaster Risk Reduction
framework and building resistance to flooding is a top priority within the country’s INDC submission.
The Directorate of Civil Protection (DPC) under the Ministry of Interior is the agency responsible for
the coordination of DRM activities in Senegal. DPC acts as the secretariat of the national platform for
the prevention and reduction of major disaster risks which was established in 2008. It is responsible
for promoting the integration of DRM into national development policies, plans and strategies.
The 2009 floods (affecting Dakar and surrounding areas primarily) appear to mark a new start for the
Senegal Government, with three steps to commit permanently to a sustainable recovery and flood
management policy. These three steps were:
1. Assessing damage, losses and post disaster needs (PDNA) for 2009;
2. The storm water management and climate change adaptation project;
3. The Ten-Year Flood Management program (PDGI, 2012-2022).
Among other projects, the most important ones are the Plan Jaxaay, that aims at resettling people
living in low-lying, flood-prone areas, launched in 2006 and the emergency phase of the PDGI that
also included 100 million USD worth of investment in infrastructure.
• In recognition of the impacts of regular flooding, Dakar city government has attempted an
innovative flood control program that involved the construction of water basins. The water
basins were part of the urban planning project called Plan Jaxaay, which relocated people
from flood-prone areas alongside efforts to channel storm water into these catchment basins.
The first basin in Pikine was built in 2007. The design included pumps that were to move
water from the basins into lake systems and eventually to the Atlantic Ocean, but these plans
were not fully realized, and the basins were never fully connected to each other or to the coast
(Hungerford et al. 2019).
• The Ten-Year Flood Management Program for 2012 to 2022, known as PDGI, is managed by
the Ministry for Restructuring and Managing Flood Zones (MRAZI), with an estimated cost of
18 million USD, and includes a 2012-2013 emergency phase, a short-term phase in 2014-2016
and a medium and long-term phase 2017-2022. This program has four main components:
1. resettlement of flood victims in furnished and equipped areas, providing an improved
living environment;
2. installation of storm water drainage;
3. restructuring of urban areas and flood-prone districts;
4. improvement of land-use planning policy and development of new urban centers.
Among the further on-going and recently completed projects and studies on flood management are the
following:
• CLUVA - CLimate change and Urban Vulnerability in Africa: FP7 Environment, 2010 –
2013: CLUVA’s analysis of extreme rainfall events, based on climate projection data until
2050, suggests that intensity and frequency of extreme events will significantly increase and
enhance flood risks in the future (Coly et al. 2011; Coly et al. 2012).
• In 2010, the Storm Water Management and Climate Change Project (“Projet de Gestion des
Eaux Pluviales – PROGEP”) was launched which was financed mainly by the World Bank.
PROGEP is an implementation plan of the PDD (Plan Directeur de Drainage de la région de
Dakar) for Pikine and Guédiawaye Departments. The Government designated the Municipal
Development Agency (ADM) with the preparation and implementation of the five-year
project (2013-2017, later extended to 2019). The objective of PROGEP was to improve storm
water drainage and flood prevention in peri-urban Dakar for the benefit of local residents.
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PROGEP aims at reducing floods through an integrated and sustainable approach. It is being
implemented together with priority measures such as:
1. the preparation of a master plan for storm water drainage and the construction of
drainage structures;
2. construction of storm water drainage on the outskirts of Dakar;
3. mapping of flood risks and within detailed urban plans (PUD);
4. developing a flood prevention Geographic Information System (GIS);
5. involving communities in flood reduction and climate change adaptation through
information campaigns to raise public awareness and support micro-projects for
reducing local flood risks.
Figure 35: A pump to clear out flooded streets of the Pikine neighbourhood of the Senegal capital Dakar
PROGEP measures (Photo Mamadou Lamine Camara, Agence de développement municipal (ADM)
Dakar)
• The Government of Senegal has been making substantial efforts to control urban growth by
preparing Urban Development Master Plans. Those successive plans, however, have failed to
control urban growth fully due to non-compliance with the land use guidelines set forth,
especially in the suburbs where the development of large irregular settlements took place on
flood areas, mainly in the Niayes area. The Dakar Urban Development Master Plan by the
Horizon 2025 was prepared in 2001 and approved in 2009. It had the ambition of rebalancing
the regional structure by creating new urban poles outside the existing Dakar agglomeration.
The 2025 Master Plan has not been effective because it was not based on an accurate
understanding of local conditions, lacking the sharing of information with all stakeholders
(JICA 2016). Therefore, supported by the Japan International Cooperation Agency (JICA), the
“Project for Urban Master Plan of Dakar and the Neighboring Area for 2035” was realized
from August 2014 to January 2016 with the following objectives:
(a) to prepare an urban development master plan for Dakar Region and the neighboring
area for the target year of 2035;
(b) to prepare a detailed plan for at least one selected area as a tool to realize the 2035
Master Plan;
(c) to conduct pre-feasibility studies on the priority projects to be selected as a tool to
realize the 2035 Master Plan;
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(d) to undertake the capacity development of the Department of Urbanization and
Architecture (DUA) and related ministries, organizations and local governments,
strengthening their staff capabilities so that they will be able to properly manage urban
development.
• Senegal Integrated Urban Flood Management Project coordinated by the Agence Française de
Développement (AFD) and funded by the UNFCCC Green Climate Fund (GCF) aims at
supporting Senegalese policy on flood risk management through a disaster risk reduction
perspective. GCF financing will focus on soft measures. Flood risk mapping will also be
undertaken, and assessments carried out on how to increase the resilience of urban areas.
Future risk will be reduced through hazard monitoring, and protocols developed for managing
extreme rain events. These actions will be complemented by AFD financing towards hard
investments in drainage and sanitation infrastructure in one of the most vulnerable areas of the
capital city (Pikine Irrégulier Sud). The project will also contribute to establishing a national-
scale integrated policy for disaster risk management in order to optimize investment at
national scale and most importantly deal with the risk that will never be cost-efficiently
covered by infrastructure. This approach of strengthening infrastructure and governance will
put Senegal at the cutting edge of flood-management policy in West Africa. Approved in
October 2016, the project has an estimated lifespan of 5 years.
• The "Living with Water" (“Vivre avec l'eau”) project aims to improve resilience to urban
flooding of 860 000 vulnerable persons living in ten communes in the suburbs of Dakar
through a multidisciplinary, integrated and inclusive approach, involving stakeholders at the
local and national levels. This project, implemented since May 2015 by a consortium of nine
organizations, of which the Consortium for Economic and Social Research (CRES) is lead
partner, combines infrastructure activities (with the construction of rainwater evacuation
system) to capacity building activities, and supports the development and implementation of a
better flood management strategy in the departments of Dakar, Pikine, Guédiawaye and
Rufisque. The project also helps the beneficiaries to improve their waste collection system,
supports the creation of waste related small companies and improve the environment of the
beneficiaries by building public furniture and embellishments. “Living With Water” supports
the beneficiaries in doing income generating activities, waste collection, urban gardening,
production and selling of compost etc. In each commune a flood emergency plan was
elaborated and the population trained to be able to prevent the flooding and know how to react
quickly in case of flooding.
5.2 Flood History
No major floods occurred during the 1960s, 1970s and 1980s, which were marked by a drought that
affected the whole of West Africa. Conditions returned to more humid climate in the 1990s. Flooding
did not immediately accompany the return to normal rainfall and water table levels in the 1990s but
instead began significantly in 2005.
Recent flood events as taken from global databases (Dartmouth Flood Observatory, GLIDE Disaster
Data Base, Emergency Events Database (EM-DAT), UNDRR DesInventar Sendai, ReliefWeb,
GFDRR) local sources and press releases include:
2005
In 2005, the Dakar metropolitan region received its highest rainfall in nearly 20 years. Examples for
heavy rain events in the flood season of 2005 are:
• 16th-17th August : 87.4 mm
• 19th-22nd August : 184.5 mm
• 28th-31st August : 55.2 mm
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Widespread flooding occurred across much of the region with Rufisque, Pikine and Guédiawaye being
most concerned. In Pikine, the floods displaced thousands of people and cost billions of francs
(Hungerford et al. 2019).
2007
The town of Thies (to the east of Dakar) reportedly received 127 millimetres of rain on the night of
13th August. Heavy Rains and floods were reported as well in the capital, Dakar (GLIDE).
2008
From September 1st to 4th, Dakar received 133mm of rain, twice as much as would normally be
expected (GLIDE). Thousands were affected by flooding in more than 40 neighborhoods across
Senegal, including 21 Dakar suburbs. Dakar neighborhoods affected include Pikine, Guédiawaye,
Thiaroye and Diamaguène (Gambia News 2008).
2009
In August and September 2009, it was about 360 000 people who were directly affected by floods in
Pikine and 22 000 people in Guédiawaye; respectively 44% and 7.2% of the population in both
departments (Ndiaye et al. 2016).
Figure 36: In the "streets" of Dakar, 17/09/2009 - Photo: SOS Archives
The Post- Disaster Needs Assessment (PDNA), funded through the Global Facility for Disaster
Reduction and Recovery (GFDRR), estimated damage and losses to total of approx. 103 million USD
nationwide, of which 82 million USD was for damage and loss in the Dakar region alone. An
estimated 30,000 houses were affected in the Dakar region, most of which were uninhabitable
permanently and often abandoned (Cissé 2018).
2010
Due to heavy rainfall since September 2, 2010, large areas in Senegal were affected by floods. One of
the most affected areas was the capital Dakar with its suburban areas, affecting more than 30,000
households. According to information received from UN-OCHA some roads as well as critical
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infrastructure were blocked by water (SAFER Rapid Mapping Activity by DLR, funded by European
Community’s 7th Framework Programme (FP7/2007-2013) under grant agreement No. 218802).
2012
Since the beginning of the rainy season in July 2012, torrential rains have caused local flooding in
several areas of Senegal, including St. Louis (North), Bambey (center) and Dakar (GLIDE).
The emergency relief plan (ORSEC) was activated after the heavy rains of August 26th. Due to heavy
flooding, 26 deaths, 264,000 affected people and 7,737 damaged houses were reported in Senegal.
Floods displaced over 5,000 families (over half from the regions of Dakar and Matam) and
contaminated 7,700 drinking water sources.
Figure 37: Flooded settlement in the Department of Pikine, August/September 2012 (Photo: Steve
Cockburn)
Figure 38: Flooding in Pikine, Sptember 2012 (Senegal7.com)
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2013
In September, an estimated 163,000 people have once again been affected by severe flooding across
Senegal. Above average rains triggered damaging floods that affecting an estimated 100,000 people.
Nearly the entire south-western part of the country was inundated: from the capital of Dakar to the
regions of Mbour, Fatick, Djilor, Passy, Kaffrine, and Kaolack. (OCHA 2013).
2015
In August and September, torrential rains in Senegal triggered severe floods. According to
radio reports, more water fell within two hours than over a 45-day average. Dakar suburbs
(e.g. Pikine, Guédiawaye, N’gor) were most concerned once again. However, in Pikine
measures taken within the frame of the Vivre avec l'eau / Live with water project with the
installation of a pilot drainage system proved to be successful as the runoff was redirected and
captured in low-lying natural basins.
2016
July to August: extreme rainfall causing floods (GLIDE)
2018
September: flooding due to heavy rainfall (GLIDE)
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5.3 EO Data Used
In general, there are two types of data available for this purpose: optical and Radar data. Available HR
optical data from the sensors Landsat 5, Landsat 8, and Sentinel-2 covering the period from 2009 to
2018 was downloaded and analysed with regard to regional and/or local flooding.
The following datasets were used for flood extent mapping:
• Landsat 5, recorded on 22/10/2009
• Landsat 5, recorded on 25/10/2010
• Landsat 5, recorded on 12/10/2011
• Landsat 8 recorded on 01/10/2013
• Landsat 8 recorded on 21/11/2014
• Landsat 8 recorded on 08/11/2015
• Sentinel 2 recorded on 30/10/2016
• Sentinel 2 recorded on 10/10/2017
• Sentinel 2 recorded on 15/10/2018
VHR Imagery covering the core city area was provided by the Project Coordinators:
• Mosaic: QuickBird-2 recorded on 07/11/2005, 06/05/2006, 21/12/2006,
• Mosaic: Pleiades recorded on 01/03/2018 and 02/03/2018
This data gives a good impression of the rapid development and expansion of Dakar but no
information about flooding could be obtained as VHR data was acquired in dry periods.
After experiences from other urban areas (e.g. Saint Louis, Senegal), no Radar data was downloaded
and processed. The data tend to give unreliable results in urban environment due to the high number of
low backscattering objects.
The flood risk product is a combination of hazard with Land Use / Land Cover (LULC) information.
The latter is derived from the Very High Resolution (VHR) satellite data based LULC classification
produced by NEO covering the Core City Area.
The Larger Urban Area LULC classification is based on Landsat 7 Mosaic (14/01/2006, 03/03/2006,
12/03/2006, 28/03/2006) and Sentinel 2 data (27/02/2018) respectively. Thus, these datasets are also
indirectly used for the flood risk product.
For the following events VHR imagery is available in Google Earth and was used for visual
interpretation of flood extents (cf. Figure 40 and Figure 41):
• October 2009 (recorded on 14/10/2009)
• December 2010 (recorded on 27/12/2010)
• October 2011 (recorded on 14/10/2011)
• October 2012 (recorded on 16/10/2012)
• October 2013 (recorded on 20/10/2013)
• September 2014 (recorded on 02/09/2014)
• August 2015 (recorded on 27/08/2015)
• October 2016 (recorded on 07/10/2016)
• August, September 2017 (recorded on 13/08/2017, 05/09/2017)
• October 2018 (recorded on 15/10/2018)
For physiographic analyses two different Digital Terrain Models were available:
• the 5m Digital Terrain Model of Dakar (BaseGéo Sénégal, (http://www.basegeo.gouv.sn/)
based on Urban Database (UDB) product (covering part of Core City Area only, cf. Figure
39);
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• the ALOS Global Digital Surface Model "ALOS World 3D - 30m (AW3D30)", version 2.1
(©JAXA) was used for physiographic analyses.
Figure 39: Coverage of the 5m Digital Terrain Model of Dakar (© BaseGéo Sénégal)
5.4 Short description of methodological approach
Historic flood extent mapping
Flood extent mapping based on EO data heavily depends on available datasets as well as on types of
floods in focus. Whereas there is a good chance to identify river floods and long-term water
stagnation, normally no information regarding short-term local floods (flash-floods) can be obtained
from EO data due to short duration and/or cloud cover. In some cases, short-term local floods can be
recorded and localized based on reports (e.g. in social media) and press releases but such inventory
never will meet the claim of being complete.
The flood extent is derived from historical optical satellite imagery of 30-meter resolution (Landsat 5
and Landsat 8) and 10-meter resolution (Sentinel 2).
The relevant datasets were corrected atmospherically applying the Dark Object Subtraction (DOS)
approach.
For defining the water extent, the water cover was classified by applying the Automated Water
Extraction Index AWEIsh (Feyisa et al. 2014) which makes use of the reflectance values of Blue,
Green, Near Infrared and Shortwave Infrared spectral bands of the Landsat 5, Landsat 8, and Sentinel-
2 sensors. The AWEIsh is an index formulated to effectively eliminate non-water pixels, including
dark built surfaces in areas with urban background. The equation is intended to effectively eliminate
shadow pixels and improve water extraction accuracy in areas with shadow and/or other dark surfaces.
The delimitation of permanent water cover (representing a high water-level during the rainy season) is
based on the EO4SD LULC classification (performed by SIRS).
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Finally, mapping of flood extents by visual interpretation of VHR imagery when available in Google
Earth was done (cf. Figure 40 and Figure 41). For Dakar, a large archive of VHR imagery is available.
Figure 40: Flooded areas in southern part of Pikine (neighbourhood of Diammaguen) in August 2015
(image recorded on 27/08/2015, © Maxar Technologies)
Figure 41: Flooded areas in southern part of Pikine (neighbourhood of Dalifort) in August 2017 (image
recorded on 13/08/2017, © Maxar Technologies)
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Flood hazard mapping
The flood hazard map was generated based on the occurrence of flood events during the past 10 years
(2009 – 2018). The map aims to give an idea about the flood presence in terms of both frequency and
extent in the city, and illustrates which part is generally flooded more often than other areas.
Water extents representing floods of small rivers as well as rainwater stagnation after heavy rains
(9 events) are based on:
• data from Landsat 5 (acquired on 22/10/2009, 25/10/2010, 12/10/2011), Landsat 8 (acquired
on 01/10/2013, 21/11/2014, 08/11/2015, both provided by the US Geological Survey), and
Sentinel-2 (acquired on 30/10/2016, 10/10/2017, 15/10/2018, provided by the European Space
Agency)
• and on visual interpretation of VHR data as available in Google Earth (10 events, see Chapter
5.1.3).
The flood hazard classification was done according to the approach selected by NEO on Cambodia
cities during EO4SD Phase 1: a “number of occurrences” was calculated by combining flood extents
as derived from HR imagery and from visual interpretation of VHR imagery. This data was classified
according to the following specifications for the hazard definition:
• area flooded once between 2009 and 2018: low hazard
• area flooded twice or three times: medium hazard
• area flooded more than three times: high hazard
However, it has to be underlined that both approaches for the flood extent identification differ
significantly and that the analysis of VHR data does not cover the peri-urban area. Furthermore, in
some cases, the areas identified as flooded in HR and VHR data respectively refer to the same event.
Therefore the “number of occurrences” may not be used for any further sub-classifications or
interpretations.
Flood risk mapping
Risk is defined as a combination of probability and consequences. A detailed and uniform land-use
map is an important prerequisite to perform flood risk calculations, since it determines what is
damaged in case of flooding.
Two different datasets regarding the urban Land Use / Land Cover were made available for this
analysis:
• LULC product generated by SIRS through EO4SD-Urban based on VHR data covering the
Core City Area (approx. 422,4 sqkm)
• LULC product generated by SIRS through EO4SD-Urban based on HR data covering the
Larger Urban Area (approx. 823,4 sqkm)
The exposition is classified following an approach developed by NEO (based on: Dasgupta et al.
2015) integrating economic costs, social damage, physical damage and flood duration. Four land use
damage levels (A, B, C, D) are defined based on this estimation.
Both land-use classification results were recoded to pre-defined categories (as given in Table 13 and
Table 14) and merged after categorization.
The flood risk product is a combination of hazard with Land Use / Land Cover (LULC) information.
The latter is derived from the Very High Resolution (VHR) satellite data based LULC classification
produced by SIRS covering the Core City Area. The Larger Urban Area LULC classification is based
on Landsat 7 and Sentinel 2. Thus, these datasets are also indirectly used for the flood risk product.
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Table 13: Land use classes and reclassification to pre-defined damage levels in Core City Area
Classes Damage Total Level
Economic
Costs
0-2
Social
Damage
0-2
Physical
Damage
0-2
Flood
Duration
0-2
Very high density continuous urban
fabric 1.5 1.5 2 1.5 6.5 D
High and medium density
discontinuous urban fabric 1.5 1 2 1 5.5 C
Low and very low density
discontinuous urban fabric 0.5 1.5 1.5 1 4.5 C
Industrial, commercial, public,
military and private units 2 0.5 1 0.5 4 B
Arterial and Collector roads 1.5 1 2 1.5 6 C
Railway 1.5 1 2 1.5 6 C
Port area 2 1 0.5 1.5 5 C
Airport 2 0.5 1.5 1.5 5.5 C
Mineral Extraction and Dump Sites 1 0 0.5 0.5 2 A
Construction sites 1 0.5 0 0 1.5 A
Land without current use 0 0 0 0 0 A
Green urban area 0.5 0.5 0.5 1 2.5 B
Sports and leisure facilties 0.5 0.5 0 0.5 1.5 A
Agricultural area 1.5 0.5 0 1 3 B
Forests and shrublands 0.5 0 0 0 0.5 A
Natural areas (grassland) 0 0 0 0 0 A
Bare soil 0 0 0 0 0 A
Wetlands 0 0 0 0 0 A
Inland and marine water 0 0 0 0 0 A
Table 14: Land use classes and reclassification to pre-defined damage levels in Larger Urban Area
Classes Damage Total Level
Economic
Costs
0-2
Social
Damage
0-2
Physical
Damage
0-2
Flood
Duration
0-2
Artificial Surfaces 1.5 1.5 2 1.5 6.5 D
Agricultural area 1.5 0.5 0 1 3 B
Forests ans shrublands 0.5 0 0 0 0.5 A
Natural areas (grassland) 0 0 0 0 0 A
Bare soil 0 0 0 0 0 A
Wetlands 0 0 0 0 0 A
Inland and marine water 0 0 0 0 0 A
The Flood Risk matrix is generated based on above code and flood hazard classified into three hazard
levels. The flood risk level is classified in four qualitative classes based on the combination of flood
hazard and land use damage as shown in Table 15.
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Table 15: Flood Hazard and Risk classification
Damage cost on land use
A B C D
Flood Hazard
1 (low) 1A 1B 1C 1D
2 (medium) 2A 2B 2C 2D
3 (high) 3A 3B 3C 3D
Flood Risk classification
Low Risk 1A 1B 2A
Medium Risk 1C 1D 2B 2C 3A 3B
High Risk 2D 3C
Very high Risk 3D
5.5 Product Description and Accuracy Assessment
There are three final layers in this product: (1) the raw data on past flood extents as derived from EO
data and ancillary data (flood history), (2) the Flood Hazard map which summarizes past flood events
and thus gives information about the likelihood of future events, and (3) the Flood Risk map
combining this data with information on urban and peri-urban land use and its damage potential in
case of flooding.
The Flood Hazard map (subset on Figure 42) displays flood hazard information (delivered in vector
format). This data is based on the occurrence of floods of the past 10 years (2009 – 2018). It takes into
account both the hazard from seasonal floods and rainwater stagnation in the rainy season as well as
from tidal waves and coast erosion. The classification in three qualitative hazard levels is expert-based
under consideration of mapped frequencies of floods.
The map aims to give an idea about the flood presence in terms of both frequency and extent in Dakar
and illustrates which part is in general flooded more often than other areas.
Since no independent reference data are available no accuracy assessment is possible. The plausibility
of the results nevertheless was evaluated on basis of local reports and press releases.
The Flood Risk Map (subset see Figure 43) displays flood risk information (delivered in vector
format). This data is based on the occurrence of floods of the past 10 years (2009 – 2018) combined
with information of the land use map provided by SIRS of this project area. It takes into account both
the hazard level and potential damages, based on different land uses. The damages are assessed on 4
aspects: economic, social, physical and flood duration.
Since this product is a direct derivation and combination of the Hazard Classification and the
Land Use Classification, its plausibility can be rated high when the mentioned input datasets are rated
as being plausible and reliable.
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Figure 42: Subset of Flood Hazard Map of Dakar (Department of Pikine) (Background Image: Sentinel 2, recorded
on 10/10/2016, European Space Agency)
Figure 43: Subset of Flood Risk Map of Dakar (Department of Pikine) (Background Image: Sentinel 2, recorded on
10/10/2016, European Space Agency)
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5.6 Results
Flood Hazard
For the Department of Grand Dakar (administrative centre, airport and port area) some parts are
indicated with low hazard (which means they were flooded once within the last 10 years): this was the
case in September 2014 in the area west of the airport (Ngor) and in September 2017 in the area north
of the port (Colobane). The floods were caused by rainwater stagnation after heavy local rainstorms.
However, most parts of Grand Dakar are not endangered by floods.
Most flood prone zones are indicated in the departments of Pikine and Guédiawaye (cf. Figure 42).
Some zones in the Niayes are flooded almost every year in the rainy season. This is not only true for
areas which are covered by different vegetation but also for densely built-up areas.
In the department of Rufisque there is also a high percentage of the area indicated as flood prone but
most of the concerned area is classified with low or medium hazard (flooded up to three times within
the last 10 years).
Only a low proportion of the larger urban area is classified as flood prone. This is also due to the fact
that the analysis of VHR data was restricted to the core urban area and analysis of flood events in the
larger urban area is based on automatic classification of HR data only.
For the following calculations the permanent water bodies as defined by the EO4SD LULC
classification by SIRS are not considered. While the share of permanent water cover in the core city
area is about 38,8%, this share is only approx. 11,6% in the peri-urban area. For the total area of
interest, a percentage of 25,6% was calculated.
With regard to the flood hazard zones it can be observed that the expansion of such zones differs
significantly in Dakar Core City Area and in the Larger Urban region: approx. 24,8% in the Core City
area, but only about 1,2% in the Larger Urban region, are classified as flood prone (cf. Figure 44 and
Figure 45): 12,25% of the Core City Area are defined as medium and high hazard zones whereas this
percentage is only 0,93% in the peri-urban region.
This is mainly because the extensive marshes to the north of Saint Louis are flooded regularly during
the rainy season and thus are classified as high- and medium-hazard areas with the selected approach.
Figure 44: Percentages of flood hazard zones in Dakar core city area
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Figure 45: Percentages of flood hazard zones in Dakar peri-urban region
Residential Urban Fabric with high damage potential (LU classes: Residential – continuous urban
fabric, Residential – discontinuous dense urban fabric, Residential – discontinuous medium density
urban fabric, Residential – discontinuous low density urban fabric, Residential – discontinuous very
low density urban fabric, Industrial, Commercial, Public) was analyzed more detailed and combined
with the Flood Hazard Map in the core city area of interest.
For the peri-urban area the LU class “Artificial Surfaces” was analyzed in detail.
The statistics for Residential Urban Fabric in the core city area is shown in Figure 46; based on these
statistics, approx. 4,7% of Dakar’s Residential Urban Fabric is situated in Medium and High Flood
Hazard Zones. Additional 11,28% of these landuse classes is situated in Low Flood Hazard Zones.
Figure 46: Proportion of Residential Urban Fabric in flood hazard zones in Dakar core city area
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Figure 47: Subset of map of Residential, Industrial, Commercial and Public Urban Fabric combined with
Flood Hazard Zoning in Dakar’s department of Pikine (Background Image: Sentinel 2, recorded on
10/10/2016, European Space Agency)
For the larger urban area only 0,17% of the landuse class “Artificial Surface” is situated in Medium
and High Flood Hazard Zones. Additional 0,15% of this landuse class is situated in Low Flood Hazard
Zones.
Flood Risk
Generally, flood risk zones are well known - but to limited degree respected and enforced. In Dakar,
following many years of drought, the population moving in from rural parts occupied parts of the
interdunal depressions (Niayes) normally under water; consequently, many districts may be flooded in
the event of a major rise in water level. Long-term rainwater stagnation in the rainy season and high
flooding risk can be observed especially in the departments of Pikine and Guédiawaye (cf. Figure 29).
Flood risk is exacerbated by rapid urbanization, insufficient drainage, and poor sewage infrastructure,
which has resulted in the settling of low-lying areas and a reduction in soil infiltration potential. Rising
sea levels and increasingly intense storms may be causes of future coastal erosion and flood risks
(JICA 2016).
It is important to be aware that the applied methodology of flood risk classification involves some
degree of human interpretation. Therefore, flood risk levels must be considered as relative metrics
rather than absolute ones.
For the following calculations the permanent water bodies as defined by the EO4SD LULC
classification by SIRS again are not considered.
As the expansion of flood risk zones is equal to that of hazard zones this results in a similar picture
with regard to the total extent of the risk zones. Because of the higher exposition of urban land use the
high and very high risk categories (accumulated) occur disproportionately high in the Core City area
(approx. 2,33% vs. 0,04% in the Larger Urban region, cf. Figure 48 and Figure 49). Also areas
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classified as medium- and low-risk zones can be observed more frequently in the core urban region
(15,57% vs. 1,15%, cf. Fig. 25 and 26). This is mainly a result of the absence of land use classes with
high damage potential in the larger urban area and in the low distribution of flood hazard zones
because of the restriction of the analysis of VHR data to the core urban area.
Figure 48: Percentages of flood risk zones in Dakar Core city Area
Figure 49: Percentages of flood risk zones in Dakar Larger Urban Area
Residential Urban Fabric with high damage potential was analyzed more detailed and combined with
the Flood Risk Map both in the Core City Area as well as in the Larger Urban Area. A subset of the
Flood Risk map combined with Residential, Industrial, Commercial and Public Urban Fabric in
Dakar’s department of Pikine is given in Figure 50. The focus of high-risk zones in landuse classes
with high damage potential can well be observed to the South of the department of Pikine.
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Figure 50: Subset of map of Residential, Industrial, Commercial and Public Urban Fabric combined with
Flood Risk Zoning in Dakar’s department of Pikine (Background Image: Sentinel 2, recorded on
10/10/2016, European Space Agency)
The statistics for Residential Urban Fabric only in Dakar Core City Area is shown in Figure 51: Based
on these statistics, about 4,08 % of Dakar’s Residential Urban Fabric is situated in high to very high
risk zones, another 10,16% in medium risk zones.
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Figure 51: Proportion of Residential Urban Fabric in flood hazard zones in Dakar Core City Area
For the peri-urban area, only 0,17% of the landuse class “Artificial Surface” is situated in high and
very high flood risk zones. Additional 0,15% of this landuse class is situated in medium flood risk
zones.
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Annex 1 – AOI Calculation based on the DG Regio approach
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AOI Calculation Methodology based on the DG Regio Approach
So far, no internationally accepted definition for the term “Urban Area” and the related Peri-Urban
area exists. Different initiatives are currently trying to address a standardised approach for defining the
term “Urban Area”. During discussions with the GPSC Co-ordinator it was considered important to
use an uniform definition for the GPSC cities in order for the cities to exchange information and share
products/experiences and conduct potential comparative studies.
In this context, it was decided to use an international approach for the demarcation of the Areas of
Interest (AOI) for mapping the GPSC cities in terms of Core Urban area and Peri-Urban area. Thus,
the approach is based on the European Union’s Directorate-General for Regional and Urban Policy
(DG REGIO) method and the definitions are described in the Regional Working Paper 2014 from the
European Commission on “A harmonised definition of cities and rural areas: the new degree of
urbanisation” (European Commission, 2014). Following the naming of the DG Regio approach, the
Urban Core is named as “High Density Core” and the Peri-Urban area is termed “Urban Cluster”.
Within the DG REGIO approach, the High Density Core is defined as contiguous grid cells of 1 km2
with a density of at least 1 500 inhabitants per km2 and a minimum population of 50 000. The Urban
Cluster is defined as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants
per km2 and a minimum population of 5 000.
The DG REGIO methodology used in the EO4SD-Urban project was slightly adjusted to Non-
European countries. For the first two GPSC cities (namely Bhopal and Vijayawada) produced within
the project the Global Human Settlement Population (GHSP) grid with a spatial resolution of 1 km
were used for the classification into “High Density Core” and ”Urban Cluster”. The raster dataset is
available for the years 1975, 1990, 2000, 2015. This dataset depicts the distribution and density of
population, expressed as the number of people per cell. The data can be downloaded under following
link http://data.jrc.ec.europa.eu/dataset/jrc-ghsl-ghs_pop_gpw4_globe_r2015a.
In 2019, a higher resolution population layer (spatial resolution of 10 m) produced by the German
Aerospace Centre (DLR) became available. The AOIs for the remaining GPSC cities (namely Melaka,
Abidjan, Dakar and Campeche) were produced based on the DLR population layer.
In the following, a more detailed description of the calculation methodology for the High-Density
Core and the Urban Cluster follows. The calculation is exemplary described on the AOI generation for
the city of Melaka.
To start with, Figure 52 shows the city of Melaka and the surrounding area. Figure 53 shows the
population distribution grid over Melaka produced by the European Commission. Figure 54 shows the
DLR population grid with 10 meter spatial resolution for Melaka while Figure 55 illustrates the
aggregated DLR population grid with 1 km spatial resolution.
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Figure 52: Satellite image showing Melaka and the surrounding area.
Figure 53: Global Human Settlement Population Layer (spatial resolution of 1 km).
Figure 54: DLR population layer (spatial resolution of 10 m).
Population Density (People / km2)
0 - 300
301 - 1.500
1.501 - 5.000
5.001 - 10.000
10.001 - 100.000
Population Density (People / 10 m2) 0.7
0.06
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Figure 55: Aggregated DLR population layer (spatial resolution of 1 km).
The High Density Core AOI for a city is created by merging the contiguous grid cells of 1 km2 with a
density of at least 1500 inhabitants per km2 and a minimum population of 50 000. In the definition of
the High Density Core the contiguity is only allowed via a vertical or horizontal connection. In a next
step, gaps are filled. Due to the coarse resolution of the population grid cells additional grid cells were
in a last step added for under estimated settlement areas. The same was done for over estimations, here
grid cells were removed.
Figure 56 shows the High Density Core AOI (red line) overlaid on the DLR population layer (left) and
on a RGB satellite image (right).
Figure 56: High Density Core area of Melaka calculated based on the aggregated DLR population layer.
The image on the left shows the AOI overlaid on the DLR population layer. On the right, the AOI is
overlaid on a RGB satellite image.
The Urban Cluster is created very similar to the High Density Core. Continuous grid cells of 1 km2
with a density of at least 300 inhabitants per km2 and a minimum population of 500 are merged
together to form the Urban Cluster. The contiguity within the Urban Cluster can also be diagonal.
After gaps are filled, areas, which were over or under estimated by the population grid were removed
or added to the AOI. Figure 57 shows the Urban Cluster AOI (magenta line) overlaid on the DLR
population layer (left) and on a RGB satellite image (right).
Population Density (People / km2)
0 - 300
301 - 1.500
1.501 - 5.000
5.001 - 10.000
10.001 - 100.000
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Figure 57: Urban Cluster area of Melaka calculated based on the aggregated DLR population layer.
In some cases, the city counterparts requested that the AOIs for the High Density Core and the Urban
Cluster follow the municipal or administrative boundary of the city. In this case, the
municipal/administrative boundary was used but enlarged in areas where the AOI created according to
the adjusted DG Regio approach was bigger.
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Annex 2 – Processing methods for EO4SD-Urban products
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Summary of Processing Methods
Urban and Peri-Urban Land Use/Land Cover and Change
The input includes Very High Spatial Resolution (VHR) imagery from different sensors acquired at
different time. The data is pre-processed to ensure a high level of geometric and radiometric quality
(ortho-rectification, radiometric calibration, pan-sharpening).
The complexity when dealing with VHR images comes from the internal variability of the information
for a single land-use. For instance, an urban area is represented by a high number of heterogeneous
pixel values hampering the use of automated pixel-based classification techniques.
For these VHR images, it is possible to identify textures (or pattern) inside an entity such as an
agricultural parcel or an urban lot. In other words, whereas pixel-based techniques focus on the local
information of each single pixel (including intensity / DN value), texture analysis provides global
information in a group of neighbouring pixels (including distribution of a group intensity / DN values
but also spatial arrangement of these values). Texture and spectral information are combined with a
segmentation algorithm in an Object Based Image Analysis (OBIA) approach to reach a high degree
of automation for most of the peri-urban rural classes. However, within urban land, land use
information is often difficult to obtain from the imagery alone and ancillary/in situ data needs to be
used. The heterogeneity and format of these data mean that another information extraction method
based on Computer Aided Photo-Interpretation techniques (CAPI) need to be used to fully characterise
the LULC classes in urban areas. Therefore, a mix of automated (OBIA) and CAPI are used to
optimise the cost/quality ratio for the production of the LULC/LUCC product. The output format is
typically in vector form which makes it easier for integration in a GIS and for subsequent analysis.
Level 4 of the nomenclature can be obtained based on additional information. These can be generated
by more detailed CAPI (e.g. identification of waste sites) or by an automated approach based on
derived/additional products. An example is illustration by categorising the density of the urban fabric
which is related to population density and can then subsequently used for disaggregating population
data.
Information on urban fabric density can be obtained through several manners with increasing level of
complexity. The Imperviousness Degree (IMD) or Soil Sealing (SL) layer (see separate product) can
be produced relatively easily based on the urban extent derived from the LULC product and a linear
model between imperviousness areas and vegetation vigour that can be obtained from Sentinel 2 or
equivalent NDVI time series. This additional layer can be used to identify continuous and
discontinuous urban fabric classes. Five urban fabric classes can be extracted based on a fully
automated procedure:
• Continuous Dense Urban Fabric (Sealing Layer-S.L. > 80%)
• Discontinuous Dense Urban Fabric (S.L. 50% - 80%)
• Discontinuous Medium Density Urban Fabric (S.L. 30% - 50%)
• Discontinuous Low Density Urban Fabric (S.L. 10% - 30%)
• Discontinuous Very Low Density Urban Fabric (S.L. < 10%)
Manual enhancement is the final post-processing step of the production framework. It will aim to
validate the detected classes and adjust classes’ polygon geometry if necessary to ensure that the
correct MMU is applied. Finally, a thorough completeness and logical consistency check is applied to
ensure the topological integrity and coherence of the product.
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Change detection: Four important aspects have to be considered to monitor land use/land cover
change effectively with remote sensing images: (1) detecting that changes have occurred, (2)
identifying the nature of the change, (3) characterising the areal extent of the change and (4) assessing
the spatial pattern of the change.
The change detection layer can be derived based on an image-to-image approach provided the same
sensor is used. An original and efficient image processing chain is promoted to compare two dates’
images and provide multi-labelled changes. The approach mainly relies on texture analysis, which has
the benefits to deal easily with heterogeneous data and VHR images. The applied change mapping
approach is based on spectral information of both dates’ images and more accurate than a map-to-map
comparison.
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Summary of Processing Methods
World Settlement Extent
The rationale of the adopted methodology is that given a series of radar/optical satellite images for the
investigated AOI, the temporal dynamics of human settlements are sensibly different than those of all
other land-cover classes.
While addressing settlement-extent mapping for the period 2014-2015 multitemporal S1 IW GRDH
and Landsat-8 data acquired at 10 and 30m spatial resolution were taken into account. Concerning
radar data, each S1 scene is pre-processed by means of the SNAP software available from ESA;
specifically, this task includes: orbit correction, thermal noise removal, radiometric calibration, Range-
Doppler terrain correction and conversion to dB values. Scenes acquired with ascending and
descending pass are processed separately due to the strong influence of the viewing angle in the
backscattering of built-up areas. As a means for characterizing the behaviour over time, the
backscattering temporal maximum, minimum, mean, standard deviation and mean slope are derived
for each pixel. Texture information is also extracted to ease the identification of lower-density
residential areas. As regards optical data, only Landsat-8 scenes with cloud cover lower than 60% are
taken into consideration (indeed, further rising this threshold often results in accounting for images
with non-negligible misregistration error). Data are calibrated and atmospherically corrected using the
LEDAPS tool available from USGS and the CFMASK software is applied for removing pixels
affected by cloud-cover and cloud-shadow. Next, a series of 6 spectral indices suitable for an effective
delineation of settlements (identified through extensive experimental analysis) are extracted; these
include – among others – the Normalized Difference Built-Up Index (NDBI), the Modified
Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index
(NDVI). For all of them, the same set of 5 key temporal statistics used in the case of S1 data are
generated for each pixel in the AOI. Moreover, to improve the detection of suburban areas, for each of
the 6 temporal mean indices also here texture information is computed. For matching the spatial
resolution of Sentinel data, the whole stack of Landsat-based features is finally resampled to 10m
spatial resolution.
To identify reliable training points for the settlement and non-settlement class, a strategy has been
designed which jointly exploits the temporal statistics computed for both S1 and Landsat data, along
with additional ancillary information. In the case of optical data, in general the most of settlement
pixels can be effectively outlined by properly jointly thresholding the corresponding NDBI, NDVI,
and MNDWI temporal mean; likewise, this holds also for non-settlement pixels. Regarding radar data,
it generally occurs that the temporal mean backscattering of most settlement samples is sensibly higher
than that of all other non-settlement classes. Nevertheless, in complex topography regions: i) radar
data show high backscattering comparable to that of urban areas; and ii) bare rocks are present, which
often exhibit a behaviour similar to that of settlements in the Landsat-based temporal statistics.
Accordingly, to exclude these from the analysis, all pixels are masked whose slope - computed based
on SRTM 30m DEM for latitudes between -60° and +60° and the ASTER DEM elsewhere - is higher
than 10 degrees.
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Support Vector Machines (SVM) are used in the classification process. However, as the criteria
defined above for outlining training samples might results in a high number of candidate points, for
AOIs up to a size of ~10000 sqkm the most effective choice proved extracting 1000 samples for both
the settlement and non-settlement class. Nonetheless, since results might vary depending on the
specific selected training points, as a means for further improving the final performances and obtain
more robust classification maps, 20 different training sets are randomly generated and given as input
to an ensemble of as many SVM classifiers; then, a majority voting is applied. Afterwards, the stacks
of Landsat-8-based and S1-based temporal features are classified separately as this proved more
effective than performing a single classification on their merger. In both cases, a grid search with a 5-
fold cross validation approach is employed to identify for each training set the optimal values for the
learning. Here, those resulting in the highest cross-validation overall accuracy are then selected and
used for classifying the corresponding study region.
A final post-classification phase is dedicated to properly combining the Landsat- and S1-based
classification maps and automatically identifying and deleting potential false alarms. To this purpose,
an advanced post-editing object-based approach has been specifically designed.
The above-described methodology has been further adapted for outlining the settlement extent in the
past solely based on Landsat-5/7 imagery available since 1984; indeed, no long-term SAR data archive
at comparable spatial resolution is freely accessible for the same timeframe (e.g., ESA ERS-1/2 data
are available from 1991 without systematic world coverage and often proved too complicated to pre-
process). In particular, for the given target period and AOI, all available Landsat imagery with cloud
cover lower than 60% is pre-processed in the same fashion as described in the previous paragraphs and
the same set of temporal statistics and texture features are extracted. Based on the hypothesis that
settlement growth occurred over time (meaning that a pixel cannot be marked as settlement at an
earlier time if it has been defined as non-settlement at a later time), all pixels categorized as non-
settlement in the 2014-2015 extent map are excluded from the analysis. Then, training samples are
derived by thresholding the temporal mean NDBI, MNDWI and NDVI; specifically, a dedicated
strategy has been implemented for automatically determining the thresholds for the 3 indices by
comparing their cumulative distribution function (CDF) for the target period with that exhibited for the
period 2014-2015. Also in this case, an ensemble of 20 SVMs is used, each one trained on a different
subset of 2000 samples (i.e., 1000 for the settlement and 1000 for the non-settlement class) and
majority voting is then employed for generating the final map. It is worth noting that, when deriving
the past settlement extent for multiple times, both the masking and threshold adaptation are performed
on the basis of the results derived for the next target period.
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Summary of Processing Methods
Percentage Impervious Surface
Imperviousness product is intended to represent the impervious surfaces because of urban
development, layers of completely or partly impermeable artificial material (asphalt, concrete, etc.)
and infrastructure construction. Therefore, the Imperviousness Degree (IMD) or Soil Sealing (SL)
information can be produced relatively easily based on the Urban Extent derived from the baseline
LULC information product and the linear model between impervious areas and vegetation presence
that can be determined and characterized from Landsat or Sentinel-2 NDVI time series.
More precisely, the raster product is generated at 10m - 30m spatial resolution by properly exploiting
Landsat-4/5/7/8 or Sentinel-2 multitemporal imagery acquired over the study area within a given time
interval of interest in which no relevant changes are expected to occur (typically a time period of 1-2
years allows to get very accurate results). Each acquired EO data is pre-processed (ortho-rectification,
radiometric calibration, pansharpening, cloud-masking). Then, the Normalized Difference Vegetation
Index (NDVI) is extracted for each image within the urban mask (corresponding to Urban Extent
product). NDVI is inversely correlated with the amount of impervious areas, i.e. the higher the NDVI
is, the higher the expected presence of vegetation, hence the lower the corresponding imperviousness
degree. The core idea is to compute per each pixel its temporal maximum which depicts the status at
the peak of the phenological cycle. It is worth noting that for different pixels in the study area,
different number of scenes might be available.
However, in the hypothesis of sufficient minimum number of acquisitions unavailable for computing
consistent statistics, this does not represent an issue. Indeed, in this framework, it is also possible to
get spatially consistent datasets useful for the desired analyses, even when investigating large
territories. Areas associated with different levels of impervious surfaces are then extracted by visual
interpretation from data sources with higher spatial resolution (e.g. VHR imagery, Google Earth
imagery). OSM layers or information derived from in-situ campaigns are other auxiliary data sources
which can also be used for this purpose. At the end, reference data are extracted in various parts of the
study region and then rasterized and aggregated at the spatial resolution of input EO data.
A support vector regression SVR module is then used for properly correlating the resulting training
information with the temporal maximum NDVI to finally derive the Percentage of Impervious Surface
(PIS) or Imperviousness Degree (IMD) for the entire AOI. Specifically, 8bit integer values from the
raster product range from 0 (no impervious surface in the given pixel) to 100 (completely impervious
surface in the given pixel).
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Summary of Processing Methods
Urban Green Areas
The location and extent of green areas are determined within the product of urban land use/ land cover
at Level I. Urban green areas refer to land within and on the edges of a city that is partly or completely
covered with grass, trees, shrubs, or other vegetation. This includes public parks, private gardens,
cemeteries, forested areas as well as trees, river alignments, hedges etc. The product delivered within
EO4SD-Urban project thus provides accurate information (1 m resolution) on the spatial location and
extent of the green areas located within the Urban Extent (Level I class: 1000) derived from the
baseline LULC information product.
Detecting and monitoring urban green coverage needs very high resolution optical satellite images,
which explains the product generation over the Core Urban Area of AOI only. The same images have
been logically used for generating the LULC information product. Consequently, the usual preliminary
quality check and pre-processing tasks were already implemented.
Urban Green Areas have been detected using automated supervised classification method. More
precisely, each single multispectral VHR scene has been classified by specifying the most appropriate
algorithm and class number. Then, pixel units from the classes considered as representing green areas
have been combined into 1 single class. From this operation results the required binary raster product.
At this stage, it only remains necessary to apply some post-processing steps:
• Morphological filter is applied to fill small gaps within the green areas (caused by shadow)
• Resampling of the data to the provided spatial resolution of 1m
• Removing small pixel groups under the minimum mapping unit.
• Integrating the information provided by the LULC product (e.g. class Urban Parks,
Cemeteries).
• Validation of Mapping results
Furthermore, using archive very high resolution images, current and historic extent of urban green
areas are compared to identify their temporal evolution – extent growth or reduction. Quality control
and accuracy assessment tasks are performed by means of visual interpretation considering also the
LULC dataset.
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Summary of Processing Methods
Flood Risk
Historic flood extent mapping
The flood extent is derived from historical optical satellite imagery of 30-meter resolution (Landsat 5
and Landsat 8) and 10-meter resolution (Sentinel 2).
The relevant datasets were corrected atmospherically applying the Dark Object Subtraction (DOS)
approach.
For defining the water extent the water cover was classified by applying the Automated Water
Extraction Index AWEIsh (Feyisa et al. 2014) which makes use of the reflectance values of Blue,
Green, Near Infrared and Shortwave Infrared spectral bands of the Landsat 5, Landsat 8, and Sentinel-
2 sensors. The AWEIsh is an index formulated to effectively eliminate non-water pixels, including
dark built surfaces in areas with urban background. The equation is intended to effectively eliminate
shadow pixels and improve water extraction accuracy in areas with shadow and/or other dark surfaces.
The delimitation of permanent water cover (representing a high water-level during the rainy season) is
based on the EO4SD LULC classification.
Finally, mapping of flood extents by visual interpretation of VHR imagery as available in Google
Earth was done.
Flood hazard mapping
The flood hazard map was generated based on the occurrence of flood events during the past 10 years.
The map aims to give an idea about the flood presence in terms of both frequency and extent in the
city, and illustrates which part is in generally flooded more often than other areas.
Water extents representing floods triggered by the small watercourses as well as rainwater stagnation
after heavy rains are based on
• data from Landsat 5, Landsat 8 (provided by the US Geological Survey), and Sentinel-2
(provided by the European Space Agency)
• and on visual interpretation of VHR data as available in Google Earth
The flood hazard classification was done according to the approach selected by NEO on Cambodia
cities during EO4SD Phase 1: a “number of occurrences” was calculated by combining flood extents
as derived from HR imagery and from visual interpretation of VHR imagery. This data was classified
according to the following specifications for the hazard definition:
• area flooded once between 2009 and 2018: low hazard
• area flooded twice or three times: medium hazard
• area flooded more than three times: high hazard
It has to be underlined that both approaches for the flood extent identification differ significantly and
that the analysis of VHR data does not cover the peri-urban area. Furthermore, in some cases, the areas
identified as flooded in HR and VHR data respectively refer to the same event.
Flood risk mapping
Risk is defined as a combination of probability and consequences. A detailed and uniform land-use
map is an important prerequisite to perform flood risk calculations, since it determines what is
damaged in case of flooding.
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Two different datasets regarding the urban LULC were made available for this analysis:
• LULC product generated by NEO through EO4SD-Urban based on VHR data (Mosaic:
Pleiades recorded on 01/03/2018 and 02/03/2018) covering the core city area (approx. 422.4
sqkm)
• LULC product generated by NEO through EO4SD-Urban based on HR data (Sentinel 2 data
recorded on 27/02/2018) covering the larger urban area (approx. 823.4 sqkm)
The exposition is classified following an approach developed by NEO (based on: Dasgupta et al.
2015) integrating economic costs, social damage, physical damage and flood duration. Four land use
damage levels are defined based on this estimation.
Both land-use classification results were recoded to pre-defined categories and merged after
categorization.
The flood risk product is a combination of hazard with Land Use / Land Cover (LULC) information.
The Flood Risk matrix is generated based on the classification of exposition and flood hazard. The
flood risk level is classified in four qualitative classes based on the combination of flood hazard and
land use damage.
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Annex 3 – Filled Quality Control sheets
Quality Control Sheets for the following products are provided in the form of independent
documents:
• Urban and Peri-Urban Land Use / Land Cover
• Urban Green Areas
• Flood History and Risk
o Flood Extent
o Flood Hazard
o Flood Risk